Substack ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at Substack? The Substack ML Engineer interview process typically spans technical and product-focused question topics and evaluates skills in areas like machine learning system design, model deployment, data pipeline architecture, and communicating complex ML concepts to diverse audiences. Interview prep is especially important for this role at Substack, as candidates are expected to demonstrate hands-on expertise in developing scalable ML solutions, integrating models into production environments, and collaborating cross-functionally in a fast-moving startup setting.

In preparing for the interview, you should:

• Understand the core skills necessary for Machine Learning Engineer positions at Substack.
• Gain insights into Substack’s ML Engineer interview structure and process.
• Practice real Substack ML Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Substack ML Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Substack is a leading platform that empowers independent writers and creators to publish, monetize, and grow their audiences through subscription-based newsletters and digital content. Operating within the media and technology industry, Substack’s mission is to enable independent expression and offer a sustainable business model for creators, free from traditional advertising constraints. The company serves millions of readers and thousands of writers worldwide, fostering vibrant communities around diverse topics. As an ML Engineer, you will directly contribute to enhancing Substack’s products by integrating advanced machine learning solutions, supporting the company’s goal to innovate and improve creator and reader experiences.

As an ML Engineer at Substack, you will lead the adoption and integration of machine learning technologies to enhance the platform’s product offerings. You’ll work closely with software engineers and data scientists to identify impactful machine learning opportunities, develop and deploy models using Python and leading ML frameworks, and integrate these solutions into Substack’s core TypeScript application. Key responsibilities include building and optimizing data pipelines, fine-tuning models for performance and scalability, and contributing to both product experiences and internal tools. This role requires autonomy, technical leadership, and cross-functional collaboration to drive Substack’s mission of empowering independent creators.

2.1 Stage 1: Application & Resume Review

Your journey at Substack begins with a thorough evaluation of your application and resume, where the focus is on demonstrated experience in developing and deploying machine learning models, ownership of end-to-end ML projects, and technical depth in Python, deep learning frameworks, and data pipeline design. Highlighting prior impact on product-focused teams, your ability to work autonomously, and experience integrating ML into production systems will set you apart. Prepare by tailoring your resume to emphasize relevant technical accomplishments, leadership in ML adoption, and cross-functional collaboration.

2.2 Stage 2: Recruiter Screen

This initial conversation, typically conducted by a recruiter or talent partner, centers on your motivation for joining Substack, your alignment with the company’s mission, and an overview of your technical and collaborative background. Expect to discuss your interest in independent media and your approach to integrating ML into consumer-facing products. To prepare, articulate your passion for Substack’s vision, clarify your career trajectory, and be ready to succinctly describe your most impactful ML projects.

2.3 Stage 3: Technical/Case/Skills Round

In this round, you’ll engage with ML engineers or data scientists in a deep dive into your technical expertise. You may encounter a blend of live coding exercises, system design problems (such as building scalable data pipelines or integrating ML models into a TypeScript codebase), and case studies that evaluate your approach to model selection, tuning, and deployment. You could also be asked to solve algorithmic challenges (e.g., implementing logistic regression from scratch, designing a feature store, or optimizing for imbalanced data), and to reason through practical ML system scenarios relevant to Substack’s business. Preparation should focus on hands-on proficiency with Python, familiarity with ML frameworks like PyTorch, data engineering, and clear communication of your technical decisions.

2.4 Stage 4: Behavioral Interview

This stage assesses your ability to collaborate within small, dynamic teams, your leadership in driving ML initiatives, and your adaptability in ambiguous settings. Interviewers—often engineering managers or cross-functional peers—will probe for examples where you independently led projects, navigated challenges in data projects, communicated complex findings to non-technical stakeholders, and upheld high standards in production environments. Prepare by reflecting on past experiences where you exceeded expectations, resolved technical hurdles, and influenced product direction through ML.

2.5 Stage 5: Final/Onsite Round

The final round, usually conducted virtually or onsite, comprises a series of interviews with senior engineers, product managers, and sometimes company leadership. Expect a mix of advanced technical discussions (such as integrating ML models with web applications, optimizing for scale, or designing robust data architectures), product-centric case studies, and values-based questions that assess your alignment with Substack’s mission and culture. Demonstrating your ability to lead ML adoption, own integrated product experiences, and collaborate across disciplines will be key. Preparation should include mock presentations of past projects, readiness to whiteboard solutions, and thoughtful questions for your interviewers.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll engage with the recruiter to discuss compensation, equity, and benefits. This stage may involve clarifying your responsibilities, growth opportunities, and team fit. Be ready to negotiate based on your experience and the value you bring, and ensure you understand the scope of ownership and autonomy expected in the role.

2.7 Average Timeline

The Substack ML Engineer interview process typically spans 3–5 weeks from initial application to offer, with each stage taking about a week to complete. Fast-track candidates with highly relevant experience and strong alignment with Substack’s mission may progress through the process in as little as two weeks, while the standard pace allows for more in-depth scheduling and feedback loops between rounds. Timelines can vary based on team availability and the depth of technical assessments.

Next, let’s break down the types of interview questions you can expect at each stage and how to approach them strategically.

3.1. Machine Learning System Design

ML Engineers at Substack need to design robust, scalable ML systems that address real-world business challenges. These questions test your ability to translate ambiguous requirements into effective models, evaluate trade-offs, and communicate your design thinking clearly.

3.1.1 Identify requirements for a machine learning model that predicts subway transit
Begin by clarifying the business objective, defining success metrics, and outlining relevant features and data sources. Discuss data collection, preprocessing, model selection, and how you would evaluate and iterate on the model.

3.1.2 Designing an ML system to extract financial insights from market data for improved bank decision-making
Explain your approach to integrating external APIs, data ingestion, feature engineering, and designing models for downstream consumption. Address scalability, reliability, and how you would monitor the system in production.

3.1.3 Building a model to predict if a driver on Uber will accept a ride request or not
Describe the end-to-end process: data collection, feature engineering, model choice, evaluation metrics, and deployment. Discuss how you’d handle imbalanced data and ensure model fairness.

3.1.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Lay out the architecture for ingestion, transformation, storage, and model serving. Emphasize modularity, automation, and how you’d ensure data quality at each step.

3.2. Deep Learning & Model Evaluation

This category assesses your understanding of neural networks, model justification, and communicating complex ML concepts. Substack values the ability to choose the right architecture and explain your reasoning to both technical and non-technical audiences.

3.2.1 Justify using a neural network for a given problem
Discuss the problem characteristics that warrant deep learning, such as non-linearity or large unstructured datasets. Compare alternatives and explain why a neural network is the best fit.

3.2.2 Explain neural nets to a non-technical audience, such as children
Use analogies and simple language to break down complex concepts. Focus on the intuition behind neural networks rather than technical jargon.

3.2.3 A logical proof sketch outlining why the k-Means algorithm is guaranteed to converge
Provide a concise proof or explanation, referencing the objective function and the iterative nature of the algorithm. Emphasize logical clarity and the underlying mathematical principles.

3.2.4 Backpropagation explanation
Describe the chain rule and how gradients flow backward through a neural network to update weights. Highlight the importance of gradient descent and practical considerations like vanishing gradients.

3.3. Data Engineering & Pipelines

ML Engineers at Substack must handle large-scale data, build reliable pipelines, and optimize for performance. These questions probe your experience with ETL, data cleaning, and scalable infrastructure.

3.3.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe your approach to data extraction, transformation, and loading, focusing on scalability, error handling, and schema evolution.

3.3.2 Design a data warehouse for a new online retailer
Outline the schema, data modeling choices, and how you’d ensure efficient querying and data integrity. Discuss partitioning, indexing, and data governance.

3.3.3 Design a data pipeline for hourly user analytics.
Explain ingestion, aggregation, storage, and monitoring. Address latency, fault tolerance, and how you’d enable fast, reliable analytics.

3.3.4 Modifying a billion rows
Describe strategies for updating massive datasets efficiently, such as batching, distributed processing, and minimizing downtime.

3.4. Applied Machine Learning & Experimentation

These questions focus on your ability to design experiments, deal with real-world data challenges, and optimize ML workflows. Substack values practical experience and a strong grasp of experimentation best practices.

3.4.1 Addressing imbalanced data in machine learning through carefully prepared techniques.
Discuss resampling, synthetic data generation, and metric selection. Explain how you’d evaluate and monitor model performance on minority classes.

3.4.2 Implement logistic regression from scratch in code
Outline the algorithm, including the loss function, gradient descent, and convergence criteria. Highlight your understanding of the mathematical foundations.

3.4.3 Write a function to sample from a truncated normal distribution
Explain the logic behind sampling from a bounded distribution and how you’d ensure statistical correctness and computational efficiency.

3.4.4 Why would one algorithm generate different success rates with the same dataset?
Explore factors like random initialization, data splits, hyperparameter settings, and stochasticity in training. Emphasize reproducibility and experiment tracking.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision, and what impact it had on the business outcome.

3.5.2 Describe a challenging data project and how you handled it, including the obstacles you faced and how you overcame them.

3.5.3 How do you handle unclear requirements or ambiguity when starting a new machine learning project?

3.5.4 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a model or dashboard quickly.

3.5.5 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to address their concerns and move the project forward?

3.5.6 Walk us through how you handled conflicting KPI definitions between two teams and arrived at a single source of truth.

3.5.7 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.

3.5.8 Tell me about a time you delivered critical insights even though a significant portion of your dataset had missing values. What trade-offs did you make?

3.5.9 Describe a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.

3.5.10 How do you prioritize multiple deadlines, and what strategies do you use to stay organized when handling several projects at once?

4.1 Company-specific tips:

Familiarize yourself with Substack’s business model and mission. Understand how machine learning can drive value for independent writers and readers—think about personalization, recommendation algorithms, and content moderation. Research recent product launches and platform features to identify areas where ML could be impactful, such as newsletter discovery, audience analytics, or spam detection.

Demonstrate a genuine passion for empowering creators and independent media. Be ready to articulate how your technical expertise aligns with Substack’s vision of supporting writers and building vibrant communities. Prepare examples of how you’ve contributed to product-centric teams and driven measurable impact through ML solutions.

Understand Substack’s tech stack and workflow. Brush up on integrating ML models into TypeScript-based web applications and discuss your experience collaborating with software engineers in a fast-paced startup environment. Show that you can communicate complex ML concepts clearly to both technical and non-technical stakeholders.

4.2 Role-specific tips:

4.2.1 Practice designing end-to-end machine learning systems, from data ingestion to model deployment.
Focus on the full lifecycle of ML solutions, including requirements gathering, feature engineering, model selection, evaluation, and production integration. Be prepared to walk through real-world system design scenarios, such as building scalable data pipelines or integrating models with Substack’s core application.

4.2.2 Highlight your experience with Python, deep learning frameworks, and data pipeline architecture.
Substack values hands-on expertise with Python and leading ML libraries like PyTorch. Prepare to discuss your approach to building reliable ETL pipelines, handling heterogeneous data, and optimizing for performance and scalability. Show how you ensure data quality and automate key steps in the ML workflow.

4.2.3 Demonstrate your ability to tackle ambiguous, product-focused ML problems.
Expect open-ended questions that require translating business needs into technical solutions. Practice explaining your reasoning for model choice, trade-offs in system design, and how you iterate on models to improve product experiences. Be ready to justify your decisions and communicate the impact of your work.

4.2.4 Prepare to address practical ML challenges such as imbalanced data, experiment design, and model evaluation.
Review techniques for handling imbalanced datasets, designing robust experiments, and evaluating model performance with appropriate metrics. Be able to discuss your process for monitoring models in production and ensuring fairness and reliability.

4.2.5 Show proficiency in communicating complex ML concepts to diverse audiences.
Substack values engineers who can explain technical topics simply and persuasively. Practice breaking down neural networks, backpropagation, and algorithmic choices for non-technical stakeholders. Use analogies and clear language to convey intuition and business value.

4.2.6 Reflect on cross-functional collaboration and leadership in ML initiatives.
Think about past experiences where you led ML projects, influenced product direction, or resolved technical challenges in ambiguous settings. Prepare stories that highlight your autonomy, adaptability, and ability to drive consensus among stakeholders.

4.2.7 Be ready to discuss strategies for handling large-scale data and optimizing data infrastructure.
Substack’s scale requires efficient data engineering. Prepare to talk about designing scalable ETL pipelines, updating massive datasets, and ensuring data integrity and performance. Emphasize your experience with distributed processing and fault tolerance.

4.2.8 Prepare examples of balancing short-term product needs with long-term data integrity.
Substack moves quickly, so interviewers will want to see how you manage trade-offs between shipping fast and maintaining high standards. Share stories where you balanced deadlines, made tough prioritization decisions, and advocated for sustainable data practices.

4.2.9 Practice responding to behavioral questions about influencing without authority and aligning stakeholders.
Reflect on times you used data prototypes, wireframes, or persuasive communication to drive alignment among teams with different visions. Be ready to discuss how you navigate ambiguity, handle disagreements, and build consensus around ML solutions.

4.2.10 Prepare to showcase your organizational skills and ability to manage multiple ML projects simultaneously.
Substack values engineers who thrive in dynamic environments. Discuss your strategies for prioritizing deadlines, staying organized, and delivering results across several initiatives. Highlight tools, workflows, or habits that help you succeed under pressure.

5.1 How hard is the Substack ML Engineer interview?
The Substack ML Engineer interview is challenging and designed for candidates with strong hands-on experience in building and deploying machine learning solutions. You’ll be tested on system design, data pipeline architecture, deep learning fundamentals, and your ability to translate ambiguous business requirements into robust ML products. Expect a blend of technical depth, product intuition, and clear communication—especially in a fast-paced, startup environment.

5.2 How many interview rounds does Substack have for ML Engineer?
Substack’s ML Engineer interview process typically includes 5-6 rounds: an initial resume/application review, a recruiter screen, technical/case interviews, a behavioral interview, and a final onsite or virtual round with senior team members. Each round is designed to assess a different aspect of your technical and collaborative skillset.

5.3 Does Substack ask for take-home assignments for ML Engineer?
Take-home assignments are occasionally part of the Substack ML Engineer process, especially for candidates who need to demonstrate practical coding or system design skills. These assignments often focus on real-world ML challenges, such as building a data pipeline or implementing a model from scratch, and are designed to mirror the types of problems you’ll solve on the job.

5.4 What skills are required for the Substack ML Engineer?
Key skills include proficiency in Python, deep learning frameworks (such as PyTorch), data pipeline and ETL architecture, ML system design, and experience integrating models into production environments. Strong communication, cross-functional collaboration, and the ability to work autonomously on product-focused teams are also essential. Familiarity with Substack’s tech stack, including TypeScript integration, is a plus.

5.5 How long does the Substack ML Engineer hiring process take?
The Substack ML Engineer hiring process typically spans 3–5 weeks from application to offer. Each stage—application review, recruiter screen, technical interviews, behavioral interviews, and final onsite—generally takes about a week, though timelines can vary based on candidate and team availability.

5.6 What types of questions are asked in the Substack ML Engineer interview?
Expect a mix of machine learning system design problems, deep learning theory, data engineering and pipeline architecture, applied ML challenges (such as handling imbalanced data or implementing algorithms from scratch), and behavioral questions focused on collaboration, leadership, and product impact. You’ll also be asked to communicate technical concepts to both technical and non-technical audiences.

5.7 Does Substack give feedback after the ML Engineer interview?
Substack typically provides high-level feedback via recruiters, especially after onsite interviews. While detailed technical feedback may be limited, you can expect clarity on your strengths and areas for improvement, particularly regarding technical fit and alignment with Substack’s mission.

5.8 What is the acceptance rate for Substack ML Engineer applicants?
While Substack does not publish exact acceptance rates, the ML Engineer role is highly competitive, with an estimated 2–5% acceptance rate for qualified candidates. Demonstrating hands-on ML expertise, product intuition, and strong communication skills will help you stand out.

5.9 Does Substack hire remote ML Engineer positions?
Yes, Substack offers remote opportunities for ML Engineers, with some roles requiring occasional visits to the office for team collaboration and product workshops. The company values flexibility and autonomy, making it ideal for engineers who thrive in distributed and dynamic environments.

• Related guides:
  • Substack interview questions
  • Machine Learning Engineer interview guide
  • Top Machine Learning interview tips

Ready to ace your Substack ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a Substack ML Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Substack and similar companies.

With resources like the Substack ML Engineer Interview Guide, the Machine Learning Engineer interview guide, and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!