Jane Street Software Engineer Interview Questions + Guide in 2025

At Jane Street, software engineers work at the heart of trading, building low-latency systems that keep global markets moving. The culture is deliberately flat and collaborative, with engineers partnering directly with traders and researchers to solve complex problems in real time. This mix of technical challenge and open teamwork shapes both the role and the day-to-day experience at the firm.

Role Overview & Culture

Day-to-Day Responsibilities

• Build and maintain low-latency, performance-critical systems that support global trading operations.
• Design distributed infrastructure and optimize data pipelines for reliability and speed.
• Collaborate directly with traders, researchers, and developers to iterate on tools and systems.
• Take ownership from design through deployment, proactively identifying and fixing issues.

Culture

• Flat, low-ego environment with minimal hierarchy.
• Strong emphasis on intellectual humility, collaboration, and respect.
• Feedback-rich culture where ideas matter more than titles.
• Autonomy balanced with natural mentorship and learning opportunities.

Team Setting

• Engineers work in small, cross-functional teams that include traders and quants.
• Frequent pair programming and close collaboration to refine solutions quickly.
• Iterative problem-solving with direct input from end users.

Expectations

• Clarity of thought and logical reasoning under time pressure.
• Strong fundamentals in functional programming, particularly OCaml.
• Ability to explain trade-offs, articulate design decisions, and adapt in real time.
• Full accountability for performance, correctness, and resilience of systems.

Unique Perks

• Direct exposure to trading decisions and their immediate impact.
• Opportunity to master OCaml and functional programming at scale.
• Compensation among the highest in both tech and finance.
• Growth environment where junior and senior engineers contribute meaningfully from day one.

Why This Role at Jane Street?

What makes a software engineering role at Jane Street especially compelling is the convergence of technical innovation and real-world impact. The firm’s entire trading infrastructure is written in OCaml, giving engineers a rare chance to apply functional programming in a high-stakes environment where correctness and performance are paramount.

Compensation is among the most competitive in the industry—often surpassing FAANG levels—thanks to a profit-sharing model and a structure that rewards contribution over tenure. Beyond pay, the role offers true ownership: engineers design, test, and ship solutions that directly shape trading outcomes, free from bureaucracy or rigid promotion ladders. A flat hierarchy ensures that ideas rise quickly, while mentorship flows naturally through collaborative debugging, peer reviews, and internal knowledge-sharing.

Overview of the Interview Process

Application & Recruiter Screen

The Jane Street software engineer interview begins with a traditional application or referral. A strong resume, especially one highlighting algorithmic thinking, systems work, or functional programming, can catch the recruiter’s attention.

If selected, you’ll be contacted for an initial recruiter screen, which is typically a conversational call aimed at understanding your background, motivation, and familiarity with Jane Street’s engineering style. Some candidates report being asked why they’re interested in working in OCaml or in a finance-adjacent environment. It’s also a chance for the recruiter to prep you for what to expect next.

Online Coding Assessment

After passing the initial screen, candidates are invited to a 60-minute online coding assessment, a core component of the Jane Street coding interview process. This round typically involves solving two to three algorithmic problems in OCaml, the firm’s primary language. Prior OCaml experience is not required, and Jane Street provides a brief tutorial or sandbox environment to help you get comfortable with the syntax.

The problems focus on recursion, functional programming patterns, list manipulation, and combinatorics—think recursive tree traversals, subset generation, or designing a parser-like state machine. While they may resemble standard coding challenges, the problems are often framed in a way that rewards correctness, clarity, and functional reasoning over hacky optimizations.

Since this round is asynchronous and doesn’t involve an interviewer, you can’t rely on verbal explanation or interaction to demonstrate your thinking. Instead, you stand out through code quality. Use clear variable names, write well-structured recursive functions, and include helpful comments when relevant.

Many successful candidates also structure their solutions modularly, breaking down complex logic into smaller helper functions that reflect thoughtful design. Robust handling of edge cases and clean base-case logic in recursion are other strong signals.

Ultimately, this round is about showing that you can write precise, bug-free code in a new paradigm under time pressure. What Jane Street is looking for is not just a working solution, but an engineer who demonstrates discipline, rigor, and a deep grasp of algorithmic thinking—even in a quiet test environment.

Tips:

• Review basics of recursion, immutability, and higher-order functions.
• Practice solving problems in a functional style, even in Python, before trying OCaml.
• Write small helper functions to show modular design and clear thinking.
• Don’t forget edge cases, test your code against empty inputs, single-element lists, or extreme values.

Technical Phone Rounds

If you pass the online coding assessment, the next stage in the Jane Street software engineer interview process is one or two technical phone interviews. These are live, collaborative sessions conducted over video call or a shared editor, with a focus on both problem-solving depth and communication.

Unlike the online round, which is purely performance-based and silent, the phone interview emphasizes how you think out loud. You’ll be working through real-time coding problems with your interviewer, who may gently nudge you with new constraints, ask you to optimize your initial approach, or introduce edge cases to test your flexibility.

The format feels like pair programming: it’s less about speed and more about how well you reason under guidance. A key tip is to ask clarifying questions early—this not only ensures you fully understand the problem but also shows your collaborative mindset and attention to detail.

The types of questions differ in complexity and depth compared to the earlier round. While the online assessment leans on pure algorithmic tasks in OCaml (e.g. recursive list processing or generating combinations), the phone screen introduces more open-ended or layered problems. You might be asked to implement an interval scheduler, traverse a graph under certain constraints, or simulate a state machine. You’ll also encounter design-style questions where you need to define your own abstractions or choose between multiple valid approaches.

Additionally, Jane Street often sprinkles in brain-teasers or probability puzzles during this round. These aren’t just trivia—they test how you build logical arguments, use examples to validate ideas, and structure probabilistic reasoning in real time.

Tips:

• Start by restating the problem in your own words, as this shows understanding and buys thinking time.
• Use structured communication: outline your plan, then implement step by step.
• Treat your interviewer like a teammate, ask clarifying questions, explain trade-offs, and invite feedback.
• If stuck, pivot gracefully: suggest an alternate approach or sketch a brute-force version before optimizing.
• For probability puzzles, talk through assumptions clearly and check intuition with small examples.

On‑Site (Super‑Day)

The final round of the Jane Street software engineer interview process is the on-site Super-Day—a five- to six-hour deep dive into your technical ability, problem-solving approach, and team fit. This is where Jane Street’s collaborative and intellectually intense culture truly comes to life.

The day typically begins with a warm welcome and a brief introduction to the schedule. You’ll be assigned a host, often an engineer, who will guide you between interviews and serve as a point of contact throughout the day. Many candidates note that this informal interaction helps ease nerves and gives you a glimpse into the genuine collegiality of the team.

You’ll then go through five rounds, which often include:

• Data structures & algorithms: Expect whiteboard coding questions involving trees, graphs, intervals, or custom data structures. You’ll be asked not just to solve the problem, but to discuss trade-offs and edge cases.
• Systems & language design: This round may focus on memory management, type systems, or functional programming concepts (especially OCaml). You might be asked to reason through a toy system or compare implementation strategies.
• Practical engineering & debugging: In some versions of the interview, you’ll be given pseudo-code or broken code snippets and asked to debug them, simulate performance bottlenecks, or walk through how you’d refactor.
• Culture & communication fit: This isn’t a generic behavioral interview. Instead, it’s a conversation about how you learn, collaborate, and give/receive feedback. Be prepared to reflect on technical projects you’ve owned, how you’ve handled ambiguity, or how you approach code quality.
• Lunch with a team member: This is a casual but evaluative conversation. Your lunch host is usually an engineer who didn’t interview you directly, and this is your chance to ask candid questions about team dynamics, project life cycles, and growth paths. They may also be informally assessing how well you’d collaborate in the day-to-day.

Tips:

• For algorithms, practice not just solving but also discussing trade-offs and edge cases.
• In systems design, think about concurrency, memory, and type safety—OCaml knowledge helps here.
• During debugging, narrate your thought process step by step instead of silently scanning the code.
• For the culture fit, prepare STAR stories that show ownership, collaboration, and humility.
• Treat lunch as both casual and professional—be curious, ask questions, and stay authentic.
• Manage your energy: bring water, pace yourself, and reset between rounds.

Some candidates are also asked to meet briefly with a hiring manager or present a past technical project in a chalk-talk format. While not always required, this “mini-presentation” is a chance to demonstrate clear communication, structured thinking, and enthusiasm for engineering beyond solving algorithm puzzles.

Now that you know the flow, let’s unpack the actual questions you’ll face.

Whether you’re prepping for the online assessment, phone screen, or Super-Day, understanding the themes behind the Jane Street software engineer interview questions is essential. Jane Street isn’t testing rote memorization, but evaluating how you think, reason, and communicate under real-world constraints. Let’s break down the core types of questions you’re likely to encounter, including algorithms, systems design, and cultural fit.

Coding/Algorithm Questions

Many of the Jane Street SWE interview questions center on classic computer science fundamentals, approached with an emphasis on clean design, recursion, and functional thinking. Interviewers are less interested in whether you’ve memorized LeetCode solutions, and more in how you structure your logic, define abstractions, and handle corner cases.

Common Jane Street coding questions include recursive tree traversal, graph search with constraints, dynamic programming variants, and probability-based combinatorics. These aren’t necessarily trick questions, they reward precision, clarity, and the ability to reason modularly under light pressure.

Now start practicing in our dashboard! In each question dashboard, you’ll see the prompt on the left and an editor to write and run your code on the right.

If you get stuck, just click the “I need help” button to receive step-by-step hints. You can also scroll down to read other users’ discussions and solutions for more insights.

1. Select a random number from a stream with equal probability

   This is a classic reservoir sampling problem that tests your understanding of probability and algorithm design. It’s useful in scenarios where you can’t store the entire dataset in memory. Make sure you can reason about uniform randomness and iterative streaming logic. Jane Street loves probabilistic thinking, making this a highly relevant question.

   Tips:

   • Review reservoir sampling and why it ensures uniform probability without storing the full stream.
   • Be ready to prove correctness: explain the probability at each step.
   • Discuss time and space complexity—Jane Street values efficiency reasoning.
   • Walk through a small example stream to demonstrate intuition.

2. Determine if string A can be rotated to match string B

   Tackle this with an efficient string concatenation trick, checking if B exists in A + A. It’s deceptively simple but reveals how well you understand string transformations and rotation concepts. Avoid brute force; focus on insight. String manipulation problems often show up early in technical screens.

   Tips:

   • Use the A + A trick and check if B is a substring—this is the clean, efficient solution.
   • Explain why brute force rotation is inefficient.
   • Edge cases: identical strings, empty strings, length mismatch.
   • Emphasize insight over brute force—a hallmark of Jane Street interviews.

3. Determine the number of paths from the top-left to bottom-right of an NxN grid

   This is a foundational dynamic programming problem rooted in combinatorics. Learn to optimize with memoization or iterative DP to handle larger grids efficiently. It tests spatial reasoning and recursive structure breakdowns. Such problems help demonstrate structured problem-solving, a skill Jane Street highly values.

   Tips:

   • Show both recursive breakdown and DP/memoization optimization.
   • Connect to combinatorics: paths = binomial coefficient (2N choose N).
   • Explain trade-offs: recursion vs. DP vs. closed-form math.
   • Demonstrating multiple approaches signals strong problem-solving depth.

4. Return an array where each integer is greater than the previous by one

   This problem seems numeric, but it tests how you interpret array construction constraints. Think in terms of greedy or sorting strategies depending on the form of input. It’s a great test of how you balance logic and efficiency. Questions like this test your algorithmic clarity under slight pressure.

   Tips:

   • Clarify problem constraints carefully—interviewers may test interpretation.
   • Explore greedy vs. sorting strategies depending on the given input.
   • Walk through examples to confirm correctness.
   • Stress logical reasoning process even more than the final code.

5. Determine if a permutation of a given string could form a palindrome

   This is a high-signal question to test string hashing or frequency counting. Focus on recognizing when characters can be rearranged to satisfy the palindrome property. Use sets or dictionaries and ensure optimal performance. Jane Street looks for pattern recognition and data structure fluency in such prompts.

   Tips:

   • Remember the rule: at most one character can have an odd count.
   • Use hash maps or sets for O(N) solutions.
   • Highlight efficiency—no need to generate permutations.
   • Tie back to pattern recognition and succinct reasoning.

6. Find the missing number from an array spanning 0 to N

   Use arithmetic summation or XOR tricks to solve this in linear time without sorting. It’s a simple yet clever problem that tests bit-level thinking and mathematical insight. Be careful about integer overflow in your summation approach. This type of problem is very common in early coding rounds.

   Tips:

   • Compare two approaches: sum formula vs. XOR trick.
   • Discuss pros/cons: sum risks overflow, XOR avoids it.
   • Mention time/space: O(N) time, O(1) space.
   • Be ready to extend logic to related “find duplicate” or “find multiple missing” variants.

7. Determine which character in a string has the most repetitions

   A solid hashing or frequency array question. It evaluates your ability to map, count, and compare efficiently. Avoid premature optimization—clarity and correctness are key here. Jane Street values code that’s both clean and rigorous under time constraints.

   Tips:

   • Use hash maps/frequency arrays for counting.
   • Handle ties: define how you’d break them if needed.
   • Show how to track max frequency in a single pass.
   • Jane Street interviewers appreciate clarity and rigor—keep code clean, no over-complication.

System/Architecture Design Questions

In addition to algorithm problems, many Jane Street interview questions for software engineers focus on systems-level thinking and language design. Candidates may be asked to build a small type-safe parser, reason through a functional cache design, or simulate a distributed messaging system. The goal isn’t to assess raw systems knowledge, but your ability to reason about performance, immutability, type safety, and correctness—especially in the context of Jane Street’s OCaml-heavy stack.

Expect to be asked about trade-offs in memory use, threading, or lazy evaluation. You might design a simple compiler component or create a domain-specific abstraction. These questions highlight your ability to write maintainable, high-performance code in a real-time environment.

1. Design a recommendation algorithm for Netflix’s type-ahead search

   This problem involves building a low-latency recommendation engine that reacts to user input in real-time. You’ll need to think about caching, autocomplete latency thresholds, and personalized ranking algorithms. Consider Trie-based search trees and real-time scoring pipelines. Jane Street values system responsiveness, and this type of question tests both infrastructure and algorithmic depth.

   Tips:

   • Start with data structure fundamentals: Trie-based prefix search for fast autocomplete.
   • Discuss low-latency requirements—response within milliseconds, pre-computed caches.
   • Personalization: layer in ranking algorithms (click-through history, collaborative filtering).
   • Address trade-offs between freshness vs. speed—Jane Street values explicit reasoning here.

2. Scale a recommender system from thousands to millions of users

   You’re asked to identify performance bottlenecks and evolve a system to handle dramatic increases in scale. Consider memory vs. disk trade-offs, caching layers, and horizontally scalable architectures. Partitioning and model serving pipelines will likely come into play. This is relevant to Jane Street’s emphasis on performance and correctness at scale.

   Tips:

   • Show how you’d identify bottlenecks (memory-bound, disk I/O, network latency).
   • Propose horizontal scaling: sharding, distributed caches, and parallel model serving.
   • Weigh online vs. batch recommendations—when to precompute, when to compute on the fly.
   • Highlight resilience and correctness at scale—key to Jane Street systems.

3. Design a machine learning system to generate Spotify’s Discover Weekly

   
   Now start practicing in our dashboard! In each question dashboard, you’ll see the prompt on the left and an editor to write and run your code on the right.

   If you get stuck, just click the “IQ tutor” button to receive step-by-step hints. You can also scroll down to read other users’ discussions and solutions for more insights.

   Building this involves offline batch pipelines combined with real-time filtering logic for personalization. Consider embedding-based similarity, collaborative filtering, and feature refresh cadence. Model retraining frequency and low-latency result serving are key constraints. This question captures the balance between data throughput and user-facing responsiveness.

   Tips:

   • Break into offline training + online serving: embeddings, collaborative filtering, feature stores.
   • Discuss model refresh cadence—weekly updates balanced with real-time freshness.
   • Mention cold start problem and how you’d handle new users (popularity-based fallback).
   • Tie back to pipeline reliability—batch jobs need to be reproducible, testable, and consistent.

4. Build a dynamic pricing system for Airbnb based on demand and seasonality

   A great test of how to integrate streaming demand signals into real-time pricing decisions. You’ll design pipelines that aggregate short-term trends while allowing rule-based overrides and experimentation layers. The challenge is building a scalable, low-latency decision engine. Jane Street likes systems that adapt fast without compromising consistency.

   Tips:

   • Combine streaming data ingestion (real-time demand) with historical features (seasonality, location).
   • Design a decision engine with experimentation support (A/B tests, rule overrides).
   • Show you understand latency trade-offs: pricing must update quickly but safely.
   • Stress consistency and auditability—important for financial and user trust.

5. Design a podcast search engine with transcript data

   Think about vector search over text embeddings and integrating audio-to-text pipelines. You’ll need to balance latency with search accuracy and memory usage. Consider technologies like FAISS, ElasticSearch, or embedding stores. Jane Street’s engineering often requires creating lightweight yet accurate systems from complex inputs.

   Tips:

   • Outline the pipeline: audio → transcription → embeddings → searchable index.
   • Choose indexing tech: ElasticSearch for keyword search, FAISS/Annoy for semantic vector search.
   • Balance latency vs. accuracy—e.g., approximate nearest neighbor search.
   • Discuss scalability and storage: embedding stores grow fast, pruning and compression matter.

Behavioral & Culture‑Fit Questions

While Jane Street’s interviews are known for their technical rigor, they also include at least one session aimed at understanding how you approach work, learning, and collaboration. Rather than surface-level behavioral prompts, these conversations tend to explore your mindset: how you learn unfamiliar tools (like OCaml), how you respond to feedback, or how you’ve worked in collaborative settings like pair programming or shared codebases.

You might be asked about a time you disagreed on a technical decision, or how you handle ambiguity in design discussions. Ethical awareness also matters, especially when discussing the role of software in trading environments. What stands out here isn’t charisma—it’s intellectual humility, curiosity, and how well your working style aligns with Jane Street’s culture of open dialogue and mutual respect.

1. Describe a time you had to learn a completely new programming paradigm (e.g., functional programming)

   This is a great opportunity to walk through how you approached learning OCaml or another functional language. Emphasize structured self-study, mentorship, and how you adapted your problem-solving mindset. Reflect on the challenges of unlearning imperative habits. Jane Street is known for its OCaml stack, so a demonstrated curiosity and perseverance with functional tools is highly relevant.

   Example:

   “I had to pick up Haskell for a research project after only using Python and Java. At first, recursion and immutability were tough, but I studied systematically and paired with a mentor. Within weeks I built a parser with higher-order functions. It taught me how functional programming enforces correctness—why I’m drawn to Jane Street’s OCaml stack.”

2. Tell me about a time you worked through a disagreement during pair programming

   Focus on a collaborative experience where you and a partner had different design or implementation preferences. Talk about how you communicated your reasoning, listened actively, and aligned on goals. The outcome should reflect compromise or learning rather than a “win.” At Jane Street, where collaboration is core to engineering, this question reveals your team mindset and humility.

   Example:

   “My partner wanted a quick inline fix, while I preferred modular functions. I listened to his concern about time, and we agreed to ship the simpler version and refactor later. It kept us on schedule while preserving long-term quality.”

3. Have you ever had to make an ethically difficult decision in a professional or academic context?

   Share a story where you prioritized integrity over convenience—perhaps questioning data usage, rejecting a suspicious client offer, or flagging performance manipulation. Explain how you evaluated the tradeoffs and why ethics guided your response. In trading environments like Jane Street’s, this signals your reliability under high-stakes ambiguity.

   Example:

   “During an internship, I noticed benchmarks being tweaked to look faster. I flagged it and suggested showing both raw and optimized results. It was uncomfortable, but it built credibility and ensured stakeholders had the full picture.”

4. Describe a project where you had to balance speed and correctness

   Choose a fast-moving environment where bugs or latency have real consequences. Show how you prioritized test coverage, modular code, or team reviews even under deadline pressure. Mention trade-offs you made and how you learned to avoid compromising quality. Jane Street emphasizes correctness in all code—it’s not just a bonus, it’s a baseline.

   Example:

   “In a 24-hour hackathon, I built a log processor quickly but added unit tests for malformed inputs and modular design for later scaling. We delivered on time without major bugs, proving correctness can’t be sacrificed.”

5. What do you value most in a team or company culture?

   Speak honestly about what helps you thrive—clear communication, intellectual honesty, autonomy with support. Tie your values to how you think, learn, and contribute. Highlight how these values align with Jane Street’s culture of rigor, mutual respect, and thoughtful engineering. This helps them assess fit beyond technical capability.

   Example:

   “I value clear, honest collaboration where ideas matter more than hierarchy. In one project, rotating code reviews helped me learn quickly and improve quality. Jane Street’s flat, feedback-rich culture is exactly that.”

Preparing for a Jane Street software engineering interview takes more than solving LeetCode problems. Success comes from mastering core CS concepts, communicating clearly, and thinking like a functional programmer. Based on dozens of candidate accounts and industry prep insights, here are the key strategies that make a difference.

Master Functional Techniques

Many candidates go into the process unfamiliar with OCaml—and Jane Street knows that. While prior experience with the language is a plus, what matters more in the Jane Street coding interview is your ability to think functionally. That means approaching problems with recursion instead of loops, reasoning about code without side effects, and breaking solutions into small, composable functions.

You’ll want to get comfortable with core functional programming concepts like:

• Recursion and tail-recursion patterns
• Pattern matching
• Higher-order functions
• Immutability and pure functions
• Option types and type inference
• Persistent data structures (e.g. maps, lists, sets)

Candidates often describe problems involving tree parsing, subset generation, or writing interpreters—where recursion, not iteration, is key. Interviewers value correctness, clarity, and thoughtful base-case handling more than cleverness.

Compared to traditional software engineering interviews at FAANG-style companies, Jane Street places less emphasis on object-oriented design patterns, REST API design, frontend/backend separation, cloud infrastructure, or system scalability and specific language syntax (e.g. Java, Python, C++ idioms)

Instead, the focus is much closer to academic-style computing: algorithms, reasoning about types, modeling state transitions, and working in a language that enforces discipline through the type system. While tech companies may ask about building services or optimizing web-scale systems, Jane Street wants to see how you write bug-resistant logic that works the first time and can handle unusual edge cases.

Ultimately, success here comes from building elegant, predictable solutions with strong logical foundations—not from knowing the latest framework or being the fastest typist.

Tips:

• If you’re new to OCaml, start with functional kata problems (e.g. recursion-heavy Project Euler questions).
• Practice rewriting iterative solutions into recursive ones.
• Learn to think in immutable transformations (map/filter/fold instead of for-loops).
• Use small helper functions—clean decomposition impresses interviewers.

Simulate Time‑Boxed Puzzles

Jane Street’s assessments often take the form of dense, logic-heavy problems that require careful reading and efficient planning. To simulate this, you can use platforms like Interview Query, Codeforces, or structured mocks on LeetCode—but under a strict 45–60 minute timer. Practicing with this time constraint helps build the mental endurance and composure needed for both the online coding round and live interviews. Some candidates also suggested solving 2–3 OCaml-style problems per session, focusing on functional clarity and correctness.

In later rounds, especially the technical phone screens and Super-Day, brain teasers and probability puzzles may appear. These are not trick questions, but rather open-ended reasoning problems that test how you explore a problem space, quantify uncertainty, and explain probabilistic logic. While they’re not always time-boxed like coding tasks, it is worth practicing them under moderate time pressure to simulate the interview pacing and build confidence in structuring your thoughts aloud. Tools like Interview Query, Project Euler, or even probability sections from quant prep books can help sharpen this skill. The key is learning to stay calm, structure your logic clearly, and approach unfamiliar problems methodically.

Tips:

• Practice timed mocks (set a 45–60 min timer, solve 2–3 problems).
• Mix coding and probability puzzles—Jane Street interviews blend both.
• Review expected value, Bayes’ rule, and combinatorics—core probability concepts show up often.
• After each timed session, review what slowed you down—clarity is as important as correctness.

Think Out Loud

Success in phone and onsite rounds isn’t just about finding the right answer—it’s about narrating your process clearly and collaboratively. Multiple candidates mentioned being evaluated just as much on how they communicated as on what they coded. Thinking out loud allows your interviewer to follow your logic, correct misunderstandings early, and gauge how you respond to ambiguity or evolving requirements.

Several successful candidates emphasize that you should always state your assumptions up front, especially when the problem is underspecified. This shows that you’re aware of edge cases and realistic constraints. Others advise explicitly walking through small input examples—Jane Street interviewers often look for how well you can reason through concrete scenarios before generalizing.

It’s also recommended to talk through trade-offs as you go. If you consider both a brute-force and optimized approach, explain when and why you’d choose one over the other. The interview isn’t about finding one perfect solution; it’s about demonstrating how you approach design, error-handling, and refactoring. Practicing mock interviews can help reinforce this mindset and make your problem-solving process feel more natural under time pressure.

Tips:

• Start by stating assumptions up front (“Do inputs include negative numbers?”).
• Always walk through small examples—Jane Street loves seeing concrete reasoning.
• Compare approaches aloud: brute force vs. optimized, recursion vs. DP.
• Treat the interviewer like a collaborator, not a judge—invite them into your thinking process.

Brute Force → Optimize – Showcase Iterative Approach

A standout trait in Jane Street interviews is the expectation that candidates begin with a clear, workable solution—even if it’s brute-force—and then improve upon it. Interviewers want to see your first instincts and how you refine them, because that reflects how real engineering happens: solve the problem first, then make it elegant.

One effective way to structure your approach is to start with your ultimate goal in mind: clearly articulate what the output should be and what constraints matter (e.g. time, space, edge behavior). Then, build a simple but correct solution that satisfies the core requirement. Don’t rush to be clever—Jane Street values correctness and composability over premature optimization.

From there, discuss the limitations of your naive approach and consider multiple tools or strategies you might use to optimize: set lookups, memoization, tail recursion, or functional map/filter chains. Some candidates even keep a mental checklist: simulate small inputs, check base cases, discuss time complexity, then refactor or abstract.

Successful candidates often narrate this journey aloud: “Let me start with a brute-force method using nested loops. If that’s too slow, I think I can refactor it using a sliding window or prefix sum structure.” This not only shows clarity of thinking, but also gives your interviewer insight into your engineering mindset: build first, analyze second, polish last.

This iterative approach is a strong match for Jane Street’s real-world culture—engineers are expected to ship correct code, but also to understand when and how it can be made cleaner, faster, or more general.

Tips:

• Always start with a simple correct baseline—even if it’s slow.
• Explicitly state: “This works but has O(N²) complexity; let me improve it.”
• Keep a mental checklist: base cases, small inputs, complexity, then refactor.
• Show optimizations iteratively (memoization, tail recursion, set lookups).
• Narrate the trade-off thinking: when to stop optimizing vs. when to generalize.

Mock Interviews & Feedback

Mock interviews are one of the most efficient ways to build interview fluency—especially for Jane Street’s unique style. Interview Query offers curated SWE question sets, collaborative interview simulations, and detailed feedback loops. Several past candidates recommended recording yourself or practicing with someone familiar with OCaml, functional paradigms, or systems design. Don’t just focus on solving questions—hone how you present solutions, explain code, and stay composed when pushed to refine or rethink your approach.

What Is the Average Salary for a Software Engineer at Jane Street?

Jane Street SWE salary is among the highest in the tech and finance industries, often exceeding compensation packages offered by FAANG companies. While exact numbers vary by level and offer timing, new graduate software engineers at Jane Street typically report total compensation between $250K–$400K USD, with significant upside as seniority increases.

• Entry-Level / New Grad (L1)
  • Base: $150K–$200K
  • Total Compensation: $250K–$400K (includes profit-sharing bonus) (Levels.fyi)
• Mid-Level (L2 – 2–4 YOE)
  • Base: $175K–$225K
  • Total Compensation: $350K–$450K (Levels.fyi)
• Senior (L3/L4 – 5+ YOE, trading infrastructure exposure)
  • Base: $225K–$300K
  • Total Compensation: $450K–$600K+ (Levels.fyi)

How many coding rounds should I expect?

Most candidates go through three to four technical stages before receiving an offer. After your resume and recruiter screen, you’ll take a 60-minute online OCaml-based coding test. If successful, you’ll move on to one or two technical phone screens, followed by a full-day on-site Super-Day consisting of four to five in-depth interviews, covering algorithms, systems thinking, and collaborative problem-solving. Some candidates are also asked to present a past project or solve a design-style challenge.

What does a software engineer do at Jane Street?

A software engineer at Jane Street designs, develops, and maintains the firm’s internal systems and trading infrastructure. Unlike traditional software roles, engineers here work closely with traders to build high-performance tools that support real-time trading decisions, risk management, and data analysis. This involves writing highly efficient code, optimizing algorithms, and ensuring the reliability and scalability of systems that handle large volumes of financial data. Collaboration, problem-solving, and a strong understanding of both software engineering principles and financial markets are key aspects of the role.

What is the 40/20/40 rule of thumb in software engineering?

The 40/20/40 rule is a guideline for time allocation in software engineering projects:

• 40% of the time on initial planning, design, and architecture.
• 20% of the time on coding and implementation.
• 40% of the time on testing, debugging, and optimization.

This rule emphasizes that writing code is only a part of the job. A significant portion of effort goes into planning and testing to ensure robust, maintainable, and high-performance software. At firms like Jane Street, where systems handle high-frequency trading, following such principles is critical to avoid costly errors.

Is it hard to get a software engineering job at Jane Street?

Yes, securing a software engineering role at Jane Street is highly competitive. The firm looks for candidates who are not only technically exceptional but also comfortable with problem-solving under pressure, often in trading-related scenarios. Candidates typically undergo multiple rounds of interviews that test algorithmic thinking, coding skills, and sometimes mathematical reasoning or probabilistic thinking. Prior experience in high-performance computing, trading systems, or strong academic performance in computer science and related fields can significantly improve your chances.

What are the software engineering skills required to get a job at Jane Street?

To be considered for a software engineering role at Jane Street, you should have:

• Strong programming skills: Proficiency in languages like OCaml, C++, Python, or Java.
• Data structures and algorithms: Deep understanding for problem-solving and optimization.
• System design: Ability to architect scalable, reliable, and efficient systems.
• Mathematical and analytical skills: Comfort with probability, statistics, and quantitative reasoning.
• Debugging and testing: Strong debugging skills and ability to write maintainable, well-tested code.
• Collaboration: Ability to work closely with traders, researchers, and other engineers in a fast-paced environment.

Optional but good to have: experience with high-performance computing, financial systems, or open-source contributions.

Preparing for Jane Street is as much about mindset as it is about mastery. The Jane Street software engineer interview questions are designed to probe your reasoning, clarity, and ability to write clean, correct code in unfamiliar settings—not just your technical memory. If you’re intellectually curious, love solving hard problems, and thrive in collaborative environments, this is the kind of challenge worth pursuing.

Ready to take the next step? Sign up for a mock interview and explore more role-specific interview guides to deepen your prep. The right preparation can turn a tough interview into a defining opportunity.