Relativity Space ML Engineer Interview Guide

Getting ready for an ML Engineer interview at Relativity Space? The Relativity Space ML Engineer interview process typically spans multiple question topics and evaluates skills in areas like machine learning theory, model implementation, data pipeline design, and cross-functional collaboration. Interview preparation is especially vital for this role, as candidates are expected to build, optimize, and deploy ML models that directly impact advanced additive manufacturing systems and the future of aerospace technology. At Relativity Space, ML Engineers work at the intersection of hardware and software, extracting insights from diverse sensor and simulation data to drive innovation in rocket design and large-scale 3D printing.

In preparing for the interview, you should:

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

Relativity Space is an innovative aerospace company focused on revolutionizing rocket manufacturing and launch services through advanced additive manufacturing and automation. Their mission is to build the next great commercial launch company with Terran R, a reusable medium-to-heavy lift rocket, and to lead the U.S. in large-scale 3D printing technology, ultimately enabling humanity’s industrial base on Mars. Relativity designs, builds, and flies rockets to deliver customer payloads to orbit, leveraging breakthroughs in robotics, machine learning, and autonomous manufacturing. As an ML Engineer, you will drive advancements in manufacturing automation and process optimization, directly contributing to Relativity’s vision of expanding the possibilities of human experience through space technology.

As an ML Engineer at Relativity Space, you will design, implement, and evaluate machine learning models that enhance large-scale additive manufacturing systems, particularly for the Stargate 3D-printing platform. You will analyze data from sensors, cameras, and simulations to optimize material interactions, printing processes, and product performance. Collaborating with robotics engineers, data scientists, and other technical teams, you will deploy ML solutions into production environments and communicate findings to both technical and non-technical stakeholders. This role directly contributes to advancing Relativity’s innovative tech stack, supporting the company’s mission to revolutionize aerospace manufacturing and enable next-generation rocket launches.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application materials, including your resume and cover letter, by the Relativity Space talent acquisition team. They focus on your technical foundation in machine learning, programming proficiency (especially Python), experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn), and your background in engineering or scientific domains. Demonstrated experience with collaborative development tools (such as git, code review, and CICD) and a track record of extracting insights from diverse data sources are highly valued. To prepare, ensure your resume clearly highlights relevant projects—especially those involving industrial data, simulation, robotics, or additive manufacturing—and quantifies your impact.

2.2 Stage 2: Recruiter Screen

Next, you’ll have a conversation with a recruiter, typically lasting 30–45 minutes, designed to assess your overall fit for the ML Engineer role and for Relativity Space’s mission-driven culture. Expect to discuss your motivation for joining the company, your experience collaborating across teams, and your ability to communicate technical concepts to non-technical stakeholders. Preparation should include a concise summary of your career trajectory, your interest in aerospace and additive manufacturing, and examples of cross-functional teamwork.

2.3 Stage 3: Technical/Case/Skills Round

This stage consists of one or more interviews with senior ML engineers or technical leads, focusing on your machine learning expertise and problem-solving abilities. You may encounter practical coding exercises (often in Python), algorithmic challenges, and case studies related to manufacturing, robotics, or sensor data. Interviewers will evaluate your theoretical understanding of ML (including concepts like neural networks, kernel methods, and optimization), your approach to designing, implementing, and evaluating models, and your ability to build scalable ML pipelines. Preparation should center on reviewing core ML algorithms, hands-on coding practice, and readiness to discuss past projects where you built or optimized models for real-world engineering systems.

2.4 Stage 4: Behavioral Interview

The behavioral round is conducted by hiring managers or team leads and aims to uncover your collaboration style, adaptability, and communication skills. You’ll be asked to reflect on experiences working in multidisciplinary teams, overcoming data challenges, exceeding expectations, and making data-driven decisions. The focus is on your ability to present complex technical insights with clarity and to navigate ambiguity in fast-paced, innovative environments. Prepare by identifying key examples from your experience that demonstrate initiative, resilience, and effective stakeholder engagement.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves an onsite or virtual panel with multiple team members, including senior engineers, data scientists, and possibly cross-functional partners from robotics or manufacturing. You’ll engage in deep technical discussions, system design scenarios (such as building ML models for additive manufacturing or optimizing industrial processes), and collaborative problem-solving exercises. There may be a whiteboard or live coding session, as well as opportunities to discuss your approach to integrating ML models into production and your vision for advancing Relativity’s technology stack. Preparation should include reviewing the company’s products, recent breakthroughs, and preparing thoughtful questions for the team.

2.6 Stage 6: Offer & Negotiation

After successful completion of the interview rounds, you’ll enter the offer and negotiation phase with the recruiter. This stage covers compensation, equity, start date, and benefits, with transparency around Relativity Space’s total rewards package. Be prepared to discuss your expectations and any questions about career growth, learning opportunities, and team culture.

2.7 Average Timeline

The typical Relativity Space ML Engineer interview process spans 3–5 weeks from initial application to offer. Candidates with highly relevant experience—particularly in ML for manufacturing or robotics—may move through the process more quickly, sometimes in as little as 2–3 weeks. Each interview round is usually spaced a few days to a week apart, depending on team availability and candidate scheduling preferences.

Now, let’s explore the types of interview questions you can expect throughout this process.

3.1 Machine Learning Fundamentals & Model Design

Expect questions that assess your understanding of core ML concepts, model selection, and how to translate business needs into robust ML solutions. You’ll need to demonstrate both theoretical depth and practical intuition in applying algorithms to real-world problems.

3.1.1 Identify requirements for a machine learning model that predicts subway transit
Clarify the prediction target, relevant features, and data sources. Discuss model choice, evaluation metrics, and how you’d address challenges like data sparsity or seasonality.
Example answer: "I’d start by defining the prediction target, such as arrival time or passenger flow, and gather historical transit data. I’d select features like weather, events, and time of day, choose a regression or time series model, and validate using RMSE or MAE."

3.1.2 Why would one algorithm generate different success rates with the same dataset?
Discuss factors such as data preprocessing, hyperparameter selection, random seed initialization, and feature engineering. Emphasize reproducibility and controlled experiments.
Example answer: "Different success rates can result from variations in data splits, random initialization, or preprocessing steps. Ensuring reproducibility through fixed seeds and standardized pipelines is key."

3.1.3 How would you build the recommendation engine for TikTok’s FYP algorithm?
Describe your approach to user profiling, feature extraction, model selection (e.g., collaborative filtering, deep learning), and real-time system constraints.
Example answer: "I’d leverage user interaction data, extract features like watch time and engagement, and use a hybrid model combining collaborative filtering and neural networks. Real-time latency and scalability would drive system design."

3.1.4 Justify the use of a neural network over other models for a given prediction task
Explain when neural networks are preferable due to nonlinearity, high-dimensional data, or complex patterns, and highlight trade-offs in interpretability and training resources.
Example answer: "Neural networks excel when relationships are nonlinear or data is unstructured, like images or text, but I’d weigh their complexity against simpler models for tabular data."

3.1.5 Explain neural nets to kids
Use analogies and simple language to make neural networks approachable, focusing on basic concepts like learning from examples and pattern recognition.
Example answer: "A neural net is like a brain that learns by looking at lots of pictures and figuring out what makes a cat or a dog."

3.2 Deep Learning Concepts & Architectures

These questions test your grasp of advanced neural network architectures, optimization techniques, and how to apply them in production environments. Be prepared to discuss both theory and engineering trade-offs.

3.2.1 Describe the Inception architecture and its advantages
Summarize the use of multi-scale convolutions, dimensionality reduction, and how it improves efficiency and accuracy in image tasks.
Example answer: "Inception uses parallel convolutions of different sizes to capture features at multiple scales, reducing parameters with 1x1 convolutions and improving performance on image classification."

3.2.2 Explain how backpropagation works in neural networks
Walk through the process of calculating gradients, propagating errors, and updating weights during training.
Example answer: "Backpropagation computes the gradient of the loss with respect to each parameter by moving backward through the network, enabling efficient weight updates."

3.2.3 Discuss kernel methods in the context of machine learning
Describe how kernel functions enable non-linear classification and regression, and when you’d choose them over deep learning models.
Example answer: "Kernel methods transform data into higher-dimensional spaces to solve non-linear problems, useful in SVMs when deep learning is too resource-intensive."

3.2.4 Implement logistic regression from scratch
Outline the core steps: initializing weights, applying the sigmoid function, computing loss, and updating parameters via gradient descent.
Example answer: "I’d initialize weights, use the sigmoid function for prediction, compute binary cross-entropy loss, and update weights using calculated gradients."

3.2.5 Implement gradient descent to calculate the parameters of a line of best fit
Explain how to set up the loss function, compute gradients, and iteratively update parameters until convergence.
Example answer: "I’d define the mean squared error loss, compute gradients with respect to slope and intercept, and iteratively update them to minimize loss."

3.3 Data Engineering, ETL & Scalability

These questions evaluate your ability to design scalable data pipelines, handle unstructured data, and manage real-world system constraints. Emphasize reliability and efficiency in your solutions.

3.3.1 Aggregating and collecting unstructured data for an ETL pipeline
Describe how you’d extract, transform, and load unstructured data, including schema design, error handling, and automation.
Example answer: "I’d use parsing tools to extract raw data, design a flexible schema, and automate transformations to ensure consistency and scalability."

3.3.2 Design a scalable ETL pipeline for ingesting heterogeneous data from partners
Discuss modular pipeline design, data validation, and monitoring strategies for large-scale ingestion.
Example answer: "I’d build modular ETL components for each data source, validate inputs, and monitor for anomalies to ensure robust, scalable ingestion."

3.3.3 Modifying a billion rows in a database efficiently
Explain strategies for bulk updates, indexing, batching, and minimizing downtime.
Example answer: "I’d use batch processing, optimize queries with indexing, and schedule updates during off-peak hours to minimize impact."

3.3.4 Write a function to split data into training and testing sets without using pandas
Describe how to shuffle and partition raw data programmatically, ensuring reproducibility.
Example answer: "I’d shuffle the data using a random seed and slice arrays to create training and test sets, ensuring balanced splits."

3.3.5 Write a function to return names and ids for records that haven’t been scraped yet
Outline your approach to identifying missing records and retrieving relevant fields efficiently.
Example answer: "I’d compare existing IDs to the full list, filter out scraped records, and return the remaining names and IDs."

3.4 Applied Analytics & Experimentation

These questions focus on how you design experiments, analyze results, and translate insights into actionable recommendations. Demonstrate your ability to connect data analysis with business impact.

3.4.1 You work as a data scientist for a ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea. How would you implement it? What metrics would you track?
Explain your experimental design, control groups, and key metrics like customer acquisition, retention, and ROI.
Example answer: "I’d run an A/B test with a control group, track metrics like ride volume and customer retention, and analyze overall profitability."

3.4.2 What kind of analysis would you conduct to recommend changes to the UI?
Describe user journey mapping, funnel analysis, and how you’d validate hypotheses with metrics and user feedback.
Example answer: "I’d analyze click paths, identify drop-off points, and test UI changes with user segmentation and conversion tracking."

3.4.3 How would you explain a scatterplot with diverging clusters displaying Completion Rate vs Video Length for TikTok?
Interpret cluster patterns, discuss possible causes, and suggest further analyses to validate insights.
Example answer: "Diverging clusters may indicate different user segments or content types; I’d investigate underlying factors and test targeted interventions."

3.4.4 Describe an experiment to improve ETA predictions for a mapping service
Discuss experiment setup, feature selection, and evaluation metrics for measuring improvement.
Example answer: "I’d use historical trip data, test new features like traffic or weather, and compare prediction accuracy using MAE."

3.4.5 Design a dynamic sales dashboard to track branch performance in real-time
Explain dashboard architecture, real-time data integration, and key performance indicators.
Example answer: "I’d build a dashboard with live data feeds, visualize branch KPIs, and enable drill-downs for actionable insights."

3.5 Natural Language Processing & Recommendation Systems

These questions probe your experience with NLP tasks, sentiment analysis, and building recommendation engines. Highlight your approach to feature engineering and model evaluation.

3.5.1 How would you approach sentiment analysis for social media posts?
Describe preprocessing steps, model selection, and handling noisy or sarcastic language.
Example answer: "I’d clean text data, use a pretrained transformer model, and calibrate outputs with manual labeling for accuracy."

3.5.2 How would you design a podcast search engine?
Discuss indexing, relevance ranking, and user personalization.
Example answer: "I’d index podcast metadata and transcripts, rank results by relevance and popularity, and personalize using user history."

3.5.3 How would you generate personalized recommendations for a music streaming service?
Explain collaborative filtering, content-based methods, and feedback loops.
Example answer: "I’d combine user listening history with audio features, use collaborative filtering, and refine recommendations based on engagement."

3.5.4 Find the bigrams in a sentence
Describe your approach to tokenization and n-gram extraction.
Example answer: "I’d tokenize the sentence and pair consecutive words to extract bigrams for text analysis."

3.5.5 How would you aggregate and analyze podcast data for space-related topics?
Discuss data collection, topic modeling, and visualization.
Example answer: "I’d collect podcast metadata, apply topic modeling to group space-related content, and visualize trends over time."

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a specific instance where your analysis directly influenced a business or technical outcome. Highlight the impact and your communication with stakeholders.
Example answer: "I analyzed launch telemetry to identify a bottleneck in engine performance, recommended a design change, and saw a 10% improvement in reliability."

3.6.2 Describe a challenging data project and how you handled it.
Share a situation involving technical hurdles, ambiguous requirements, or tight deadlines. Emphasize your problem-solving and collaboration skills.
Example answer: "On a predictive maintenance project, I overcame missing sensor data by designing robust imputation strategies and aligning stakeholders on priorities."

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying goals, iterating with stakeholders, and documenting assumptions.
Example answer: "I schedule quick syncs with sponsors, document open questions, and deliver prototypes to refine requirements collaboratively."

3.6.4 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?
Discuss how you facilitated open dialogue, presented evidence, and reached consensus.
Example answer: "I shared model validation results, invited feedback, and adjusted the pipeline to incorporate team insights."

3.6.5 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Describe how early visuals or prototypes helped clarify expectations and avoid rework.
Example answer: "I created dashboard wireframes to gather feedback from engineering and business teams, ensuring everyone agreed on the metrics and layout."

3.6.6 Describe a time you pushed back on adding vanity metrics that did not support strategic goals. How did you justify your stance?
Explain your reasoning, how you communicated trade-offs, and the outcome.
Example answer: "I demonstrated how vanity metrics would dilute focus, presented a prioritization framework, and secured leadership buy-in for more impactful KPIs."

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Highlight your initiative, technical solution, and resulting improvements in workflow.
Example answer: "I built automated validation scripts for incoming telemetry, reducing manual checks and catching anomalies before analysis."

3.6.8 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Share your process for investigating discrepancies, validating data sources, and aligning stakeholders.
Example answer: "I traced data lineage, compared source reliability, and facilitated a cross-team review to establish a single source of truth."

3.6.9 How comfortable are you presenting your insights?
Discuss your experience tailoring presentations to technical and non-technical audiences, using visuals and clear narratives.
Example answer: "I regularly present findings to engineering and leadership, adapting my message to the audience and using visualizations to drive understanding."

3.6.10 Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?
Describe a project where you went above and beyond, detailing your initiative and the measurable impact.
Example answer: "I automated a manual reporting process, saving the team 20 hours per month and enabling faster decision-making."

4.1 Company-specific tips:

Deepen your understanding of Relativity Space’s mission to revolutionize rocket manufacturing through additive manufacturing and automation. Familiarize yourself with their flagship technologies, such as the Stargate 3D-printing platform and Terran R rocket, and be ready to discuss how machine learning can drive efficiency, quality, and innovation in aerospace manufacturing.

Research recent milestones, launches, and technical breakthroughs at Relativity Space. Be prepared to speak thoughtfully about how ML can be applied to solve challenges unique to large-scale 3D printing, material science, and autonomous production environments.

Demonstrate genuine enthusiasm for advancing the future of space technology. Relativity Space values candidates who are passionate about their vision to establish an industrial base on Mars and who can articulate how their skills will contribute to this mission.

Understand the role of cross-functional collaboration at Relativity Space. ML Engineers work closely with robotics, manufacturing, and hardware teams, so prepare examples of your experience communicating technical insights to diverse audiences and driving impact across disciplines.

4.2 Role-specific tips:

Showcase your expertise in designing, implementing, and optimizing machine learning models for real-world engineering or manufacturing systems. Prepare to discuss projects where you extracted insights from noisy sensor data, image streams, or simulation outputs—especially if they relate to robotics, industrial automation, or additive manufacturing.

Review core machine learning algorithms, including both classical models and deep learning architectures. Be comfortable explaining when to use neural networks versus other approaches, and justify your choices based on data characteristics, problem requirements, and resource constraints.

Demonstrate strong coding skills in Python and proficiency with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Practice implementing models from scratch, optimizing code for performance, and debugging complex data pipelines—these skills are frequently tested in technical interviews.

Prepare to design and explain scalable ETL pipelines for heterogeneous, unstructured data sources. Highlight your experience building robust data ingestion and transformation systems, managing large volumes of sensor or production data, and ensuring data quality in dynamic environments.

Emphasize your ability to deploy ML models into production, monitor their performance, and iterate based on real-world feedback. Relativity Space values engineers who can bridge the gap between research and deployment, so be ready to discuss challenges and best practices for integrating ML solutions with hardware and manufacturing systems.

Be ready for scenario-based questions that require you to design or troubleshoot ML systems in the context of aerospace manufacturing. Practice articulating your approach to model evaluation, feature engineering, and experiment design, especially when working with limited or noisy data.

Highlight your experience collaborating in multidisciplinary teams and driving projects from ideation to deployment. Offer concrete examples of how you’ve communicated technical concepts to non-technical stakeholders, aligned on project goals, and contributed to a high-performance engineering culture.

Lastly, prepare thoughtful questions for your interviewers about Relativity Space’s technology stack, ML infrastructure, and the future direction of their manufacturing automation. Demonstrating curiosity and a proactive mindset will set you apart as a candidate ready to make an immediate impact.

5.1 How hard is the Relativity Space ML Engineer interview?
The Relativity Space ML Engineer interview is challenging, particularly for candidates without prior experience in manufacturing, robotics, or large-scale engineering systems. You’ll be tested on your ability to apply machine learning theory to real-world problems, design robust data pipelines, and collaborate with hardware-focused teams. Expect deep dives into ML algorithms, coding exercises, and scenario-based questions tied to additive manufacturing and aerospace technology. Candidates who prepare thoroughly and demonstrate both technical depth and cross-functional communication skills have the best chance of success.

5.2 How many interview rounds does Relativity Space have for ML Engineer?
You can expect 5–6 interview rounds, starting with an application and resume review, followed by a recruiter screen. The process continues with technical/case interviews, a behavioral interview, and a final onsite or virtual panel with multiple team members. Each stage is designed to evaluate specific competencies, from machine learning expertise to cultural fit and collaboration skills.

5.3 Does Relativity Space ask for take-home assignments for ML Engineer?
Take-home assignments are occasionally used, especially to assess your ability to solve practical ML problems in the context of manufacturing or sensor data. These assignments typically involve coding exercises or case studies that mirror challenges faced by Relativity Space’s engineering teams. You may be asked to analyze a dataset, build a simple model, or design a pipeline—demonstrating your technical skills and approach to problem solving.

5.4 What skills are required for the Relativity Space ML Engineer?
Key skills include strong proficiency in Python, expertise with ML frameworks like TensorFlow, PyTorch, or Scikit-learn, and a deep understanding of machine learning algorithms and model evaluation. Experience designing scalable data pipelines, handling noisy sensor or simulation data, and deploying ML models in production environments is highly valued. Familiarity with robotics, manufacturing automation, or additive manufacturing is a major plus. Effective communication, cross-functional collaboration, and the ability to translate technical insights for diverse audiences are also essential.

5.5 How long does the Relativity Space ML Engineer hiring process take?
The typical hiring process spans 3–5 weeks from initial application to offer. Timelines can vary based on candidate and team availability, but each interview round is usually spaced a few days to a week apart. Candidates with highly relevant experience may progress more quickly, while those needing to coordinate multiple schedules should expect the process to take up to a month.

5.6 What types of questions are asked in the Relativity Space ML Engineer interview?
You’ll encounter a mix of technical and behavioral questions. Technical questions cover machine learning fundamentals, deep learning architectures, data engineering, ETL pipeline design, and applied analytics. Expect coding exercises, system design scenarios, and real-world case studies tied to manufacturing or robotics. Behavioral questions focus on collaboration, adaptability, stakeholder communication, and your ability to drive projects in fast-paced, innovative environments.

5.7 Does Relativity Space give feedback after the ML Engineer interview?
Relativity Space typically provides high-level feedback through recruiters after each interview round. While detailed technical feedback may be limited, you’ll receive insights about your fit for the role and areas for improvement. The company values transparency and strives to keep candidates informed throughout the process.

5.8 What is the acceptance rate for Relativity Space ML Engineer applicants?
The acceptance rate is competitive, estimated at 3–5% for qualified applicants. Relativity Space seeks candidates with a strong technical foundation, relevant domain experience, and a passion for advancing aerospace manufacturing through machine learning. Thorough preparation and a clear alignment with the company’s mission will help you stand out.

5.9 Does Relativity Space hire remote ML Engineer positions?
Relativity Space does offer remote options for ML Engineers, especially for roles focused on software, data, and machine learning. Some positions may require occasional onsite visits or collaboration with hardware teams, but remote and hybrid work arrangements are increasingly common. Be sure to clarify expectations during the interview process to find the right fit for your situation.

• Related guides:
  • Relativity Space interview questions
  • ML Engineer interview guide
  • “Top Machine Learning interview tips”

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

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