Joby Aviation ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at Joby Aviation? The Joby Aviation ML Engineer interview process typically spans a range of technical and conceptual question topics and evaluates skills in areas like machine learning algorithms, state estimation, fault detection, C++ proficiency, and system design. Interview preparation is especially vital for this role at Joby Aviation, as candidates are expected to demonstrate both deep technical expertise and the ability to apply advanced ML concepts to real-world problems in aerospace, autonomy, and safety-critical systems.

In preparing for the interview, you should:

• Understand the core skills necessary for ML Engineer positions at Joby Aviation.
• Gain insights into Joby Aviation’s ML Engineer interview structure and process.
• Practice real Joby Aviation 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 Joby Aviation ML Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Joby Aviation is a leading aerospace company pioneering the development of all-electric vertical takeoff and landing (eVTOL) aircraft for urban air mobility. Focused on creating safe, quiet, and efficient aerial transportation, Joby aims to revolutionize how people move within and between cities. With a strong emphasis on sustainability and cutting-edge technology, the company is advancing toward commercial passenger service. As an ML Engineer, you will contribute to the integration of advanced machine learning solutions that enhance flight safety, autonomy, and operational efficiency, supporting Joby Aviation's mission to transform urban transportation.

As an ML Engineer at Joby Aviation, you will develop and implement machine learning models to support the company’s advanced air mobility solutions. You’ll work closely with data scientists, software engineers, and product teams to analyze large datasets, improve flight automation systems, and optimize operational efficiency. Core responsibilities include designing robust algorithms for aircraft performance, predictive maintenance, and sensor data analysis. Your work directly contributes to enhancing the safety, reliability, and scalability of Joby’s electric air vehicles, supporting the company’s mission to revolutionize urban transportation with sustainable, autonomous flight technology.

2.1 Stage 1: Application & Resume Review

The initial screening at Joby Aviation for ML Engineer roles is handled by the talent acquisition team, focusing on your experience with machine learning algorithms, proficiency in C++ or Python, and relevant project work in areas like state estimation, robotics, and fault detection. Expect your resume to be evaluated for hands-on expertise in designing, implementing, and tuning ML models, as well as your ability to solve real-world engineering problems.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for a brief introductory call, typically lasting 30 minutes. This conversation covers your motivation for joining Joby Aviation, your understanding of the company’s mission in aviation and robotics, and a high-level overview of your technical background. Preparation should include a concise summary of your ML engineering experience, notable projects, and how your skills align with the company’s needs.

2.3 Stage 3: Technical/Case/Skills Round

This is a core part of the process and may involve multiple rounds with engineering managers and senior technical staff, such as GNC engineers. You can expect in-depth technical interviews focused on algorithms, whiteboard problem-solving, and coding proficiency (especially in C++). Topics often include state estimation, Kalman filter tuning, fault detection, and system design for real-time robotics or aviation applications. Prepare by reviewing key ML concepts, algorithmic approaches, and your ability to communicate solution strategies both verbally and via code.

2.4 Stage 4: Behavioral Interview

Conducted by hiring managers and potentially cross-functional team members, this round explores your collaboration, communication, and problem-solving skills. You’ll be evaluated on how you approach challenges in data projects, adapt to changing requirements, and contribute to a multidisciplinary engineering culture. Be ready to discuss past experiences where you demonstrated resilience, leadership, and the ability to make technical concepts accessible to non-experts.

2.5 Stage 5: Final/Onsite Round

The final stage typically includes onsite or virtual interviews with key stakeholders such as the engineering manager, senior GNC engineer, and other technical leaders. Expect a mix of technical deep-dives, project walkthroughs, and scenario-based discussions about system architecture, data quality improvement, and ML model deployment in aviation contexts. You may also be asked to solve real-world problems or critique your own previous work.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, the recruiter will connect with you to discuss the offer package, which includes compensation, benefits, and role expectations. You’ll have the opportunity to negotiate terms and clarify any outstanding questions about team structure or career growth within Joby Aviation.

2.7 Average Timeline

The typical interview process for ML Engineers at Joby Aviation spans 3-5 weeks from initial application to offer. Fast-track candidates with highly relevant skills and direct experience in aviation or robotics may complete the process in as little as 2-3 weeks, while standard timelines allow for a week or more between rounds to accommodate technical assessments and stakeholder availability.

Next, let’s break down the specific interview questions you’re likely to encounter throughout the process.

3.1 Machine Learning System Design & Modeling

Expect questions that assess your ability to design, evaluate, and implement machine learning systems for complex, real-world problems. Focus on articulating your modeling choices, feature engineering, and how you would validate performance in production environments.

3.1.1 Building a model to predict if a driver on Uber will accept a ride request or not
Clarify the prediction target and discuss relevant features such as driver history, location, and time of day. Outline your model selection process, evaluation metrics, and how you would address class imbalance.

3.1.2 Identify requirements for a machine learning model that predicts subway transit
Break down the problem into data sources, feature engineering, and model selection. Discuss how you would handle temporal data, missing values, and integration with existing systems.

3.1.3 Creating a machine learning model for evaluating a patient's health
Describe your approach to feature selection, handling sensitive data, and choosing appropriate algorithms. Emphasize the importance of interpretability and regulatory compliance in healthcare ML.

3.1.4 Designing an ML system to extract financial insights from market data for improved bank decision-making
Discuss the end-to-end pipeline, including data ingestion via APIs, preprocessing, and downstream analytics. Address scalability, latency, and accuracy trade-offs.

3.1.5 Model a database for an airline company
Explain how you would structure the database to support ML model training and inference. Focus on normalization, indexing for large datasets, and future extensibility.

3.2 Deep Learning & Neural Networks

These questions evaluate your grasp of neural network architectures, training strategies, and the ability to communicate complex ideas simply. Be ready to justify architecture choices and explain concepts to non-experts.

3.2.1 Explain neural nets to kids
Use analogies and simple language to break down neural networks into understandable components. Highlight how learning and decision-making occur within the network.

3.2.2 Justify a neural network
Articulate why a neural network is suitable for a given problem, referencing data complexity, non-linearity, and scalability. Compare with alternative models.

3.2.3 ReLu vs Tanh
Compare activation functions in terms of convergence speed, vanishing gradients, and suitability for different architectures. Discuss practical implications for model performance.

3.2.4 Scaling with more layers
Describe challenges and strategies when increasing model depth, such as regularization, residual connections, and computational constraints.

3.2.5 Inception architecture
Summarize the key innovations of the Inception architecture and its advantages for image processing tasks. Highlight how modularity and parallelism improve performance.

3.3 Data Engineering & Data Quality

You’ll be asked about handling large, messy datasets, improving data quality, and designing robust data pipelines. Focus on scalable solutions and your approach to cleaning, validating, and organizing data for ML.

3.3.1 How would you approach improving the quality of airline data?
Outline a systematic data profiling and cleaning process, including handling missing values and ensuring consistency. Discuss automation and monitoring strategies.

3.3.2 Describing a real-world data cleaning and organization project
Share your experience with cleaning, deduplication, and validation. Emphasize reproducibility and documentation for future audits.

3.3.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Detail how you would architect the pipeline for scalability, reliability, and adaptability to new data sources. Discuss data normalization and error handling.

3.3.4 Write a function that splits the data into two lists, one for training and one for testing.
Describe how to implement data splitting manually, ensuring reproducibility and avoiding data leakage. Consider edge cases such as imbalanced classes.

3.3.5 Modifying a billion rows
Discuss strategies for efficiently updating massive datasets, such as batching, indexing, and distributed processing.

3.4 Algorithms & Coding

Expect algorithmic questions that test your ability to implement ML methods and solve data-driven problems efficiently. Emphasize clarity, correctness, and scalability in your solutions.

3.4.1 Implement logistic regression from scratch in code
Describe the steps for coding logistic regression, including gradient descent, loss calculation, and convergence criteria.

3.4.2 Given a string, write a function to find its first recurring character.
Explain your approach to tracking character occurrences efficiently, considering time and space complexity.

3.4.3 Write a function to get a sample from a Bernoulli trial.
Detail how to simulate Bernoulli random variables and discuss use cases in ML experiments.

3.4.4 Write a function to return the names and ids for ids that we haven't scraped yet.
Describe how to efficiently compare two lists and return missing elements, considering scalability for large datasets.

3.4.5 Reconstruct the path of a trip so that the trip tickets are in order.
Discuss your algorithm for sequencing unordered events, handling edge cases and optimizing for performance.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Focus on a specific instance where your analysis directly impacted a business or engineering outcome. Highlight your role in interpreting data, making recommendations, and the measurable results.

3.5.2 Describe a challenging data project and how you handled it.
Choose a project with significant technical or stakeholder hurdles. Discuss your problem-solving approach, how you navigated obstacles, and the final impact.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying objectives, collaborating with stakeholders, and iteratively refining solutions to meet evolving needs.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Share how you facilitated open dialogue, presented data-driven rationale, and found common ground to move the project forward.

3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Discuss how you quantified new requests, communicated trade-offs, and used prioritization frameworks to maintain project integrity.

3.5.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Highlight your decision-making process for balancing speed and quality, and how you communicated risks and limitations.

3.5.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe your approach to building trust, presenting compelling evidence, and driving consensus.

3.5.8 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Explain your process for investigating discrepancies, validating data sources, and documenting your decision.

3.5.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Discuss how you iteratively built and presented prototypes to converge on a shared understanding and actionable solution.

3.5.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the tools or scripts you developed, how automation improved reliability, and the impact on team productivity.

4.1 Company-specific tips:

• Deeply familiarize yourself with Joby Aviation’s mission to revolutionize urban air mobility through all-electric eVTOL aircraft. Understand how machine learning plays a critical role in flight safety, autonomy, and operational efficiency for these vehicles.

• Research recent advancements and news about Joby Aviation, including their progress toward commercial passenger service and partnerships in the aerospace sector. This context will help you connect your ML expertise to the company’s goals during interviews.

• Learn about the unique challenges of applying machine learning in aviation and robotics, such as safety-critical operations, real-time decision-making, and strict regulatory requirements. Be ready to discuss how you would address these constraints in your work.

• Study the types of data generated by eVTOL aircraft—sensor streams, flight logs, maintenance records—and think about how ML models can extract actionable insights to improve reliability and safety.

4.2 Role-specific tips:

4.2.1 Brush up on state estimation and fault detection techniques, especially Kalman filters and their applications to autonomous vehicles.
Expect to discuss your understanding of state estimation algorithms and demonstrate how you would tune and validate Kalman filters or similar methods for real-time flight control. Prepare to explain fault detection strategies and their importance in maintaining safety and reliability in aviation systems.

4.2.2 Practice implementing core ML algorithms from scratch, with special attention to logistic regression, neural networks, and time-series models.
Interviewers may ask you to code algorithms without external libraries. Focus on writing clear, efficient code in C++ or Python, and be ready to explain your logic, convergence criteria, and choices of loss functions.

4.2.3 Prepare to design robust, scalable data pipelines for ingesting and processing heterogeneous sensor and operational data.
Be ready to discuss your experience architecting ETL systems, handling messy aviation data, and ensuring data quality at scale. Highlight strategies for cleaning, deduplication, and validation, as well as automation of data-quality checks.

4.2.4 Strengthen your ability to communicate complex ML concepts to non-expert stakeholders, including engineers and business leaders.
Practice explaining neural network architectures, activation functions, and model selection using analogies and clear language. Demonstrate your skill in justifying technical decisions and making ML solutions accessible.

4.2.5 Review system design principles for deploying ML models in safety-critical and real-time environments.
Expect questions about how you would structure ML systems for reliability, latency, and scalability in aviation. Discuss strategies for monitoring models in production and responding to unexpected data patterns or system failures.

4.2.6 Prepare examples of real-world projects where you improved data quality, automated routine checks, or resolved conflicting data sources.
Share stories that highlight your problem-solving skills and attention to detail. Emphasize how your solutions increased reliability and supported critical business or engineering decisions.

4.2.7 Practice behavioral interview responses that showcase your teamwork, adaptability, and ability to drive consensus across multidisciplinary teams.
Reflect on times when you navigated ambiguity, managed scope creep, or influenced stakeholders without formal authority. Use specific examples to demonstrate resilience, leadership, and a collaborative approach.

4.2.8 Be ready to discuss your experience with model validation, interpretability, and regulatory compliance, especially in contexts where safety and transparency are paramount.
Show that you understand the importance of building interpretable models and validating their performance rigorously, particularly when human lives or critical systems are involved.

5.1 How hard is the Joby Aviation ML Engineer interview?
The Joby Aviation ML Engineer interview is considered challenging due to its focus on both advanced machine learning concepts and their application to safety-critical aerospace systems. Candidates are expected to demonstrate expertise in algorithms, state estimation, fault detection, and robust coding (especially in C++), as well as strong problem-solving and communication skills. The technical depth and real-world relevance of questions make this process rigorous, but well-prepared candidates with a background in ML for robotics or aviation will find it rewarding.

5.2 How many interview rounds does Joby Aviation have for ML Engineer?
Typically, the process involves 5-6 rounds: an initial application and resume review, a recruiter screen, multiple technical and case interviews, a behavioral round, and a final onsite or virtual interview with engineering leadership. Each stage is designed to assess both technical proficiency and alignment with Joby Aviation’s mission and values.

5.3 Does Joby Aviation ask for take-home assignments for ML Engineer?
Take-home assignments are occasionally part of the interview process, especially when evaluating practical coding skills or system design abilities. Assignments may involve implementing ML algorithms from scratch, designing scalable data pipelines, or analyzing real-world aviation datasets.

5.4 What skills are required for the Joby Aviation ML Engineer?
Essential skills include deep knowledge of machine learning algorithms, state estimation (such as Kalman filters), fault detection methods, proficiency in C++ (and/or Python), and experience with system design for real-time or safety-critical environments. Strong data engineering, problem-solving, and communication abilities are also crucial, along with a passion for aerospace and autonomous systems.

5.5 How long does the Joby Aviation ML Engineer hiring process take?
The typical timeline is 3-5 weeks from initial application to offer, depending on candidate availability and scheduling logistics. Fast-track candidates with highly relevant experience may complete the process in 2-3 weeks, while standard timelines allow for more in-depth technical assessments and stakeholder interviews.

5.6 What types of questions are asked in the Joby Aviation ML Engineer interview?
Expect a mix of technical, behavioral, and system design questions. Technical interviews cover ML algorithms, coding in C++ or Python, state estimation, and fault detection. System design questions focus on deploying robust ML solutions in aviation contexts. Behavioral rounds assess collaboration, adaptability, and communication, often through scenario-based questions about data quality, ambiguity, and stakeholder alignment.

5.7 Does Joby Aviation give feedback after the ML Engineer interview?
Joby Aviation typically provides high-level feedback through recruiters, especially regarding overall fit and technical performance. Detailed feedback on specific technical answers may be limited, but candidates are encouraged to seek clarification on areas for improvement.

5.8 What is the acceptance rate for Joby Aviation ML Engineer applicants?
While exact figures are not public, the ML Engineer role at Joby Aviation is highly competitive, with an estimated acceptance rate of 3-5% for qualified applicants. Deep expertise in machine learning for robotics or aerospace significantly improves your chances.

5.9 Does Joby Aviation hire remote ML Engineer positions?
Yes, Joby Aviation offers remote opportunities for ML Engineers, although some roles may require occasional onsite visits for team collaboration, hardware integration, or flight testing. Flexibility depends on project needs and team structure.

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Ready to ace your Joby Aviation ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a Joby Aviation 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 Joby Aviation and similar companies.

With resources like the Joby Aviation ML 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. Whether you’re refining your approach to state estimation, fault detection, or scalable data pipelines, these resources will help you master the unique challenges of ML in aerospace, autonomy, and safety-critical systems.

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!