A Machine Learning Engineer at Rivian works at the unique intersection of advanced software engineering and cutting-edge physical hardware. In this role, you are responsible for designing, building, and deploying machine learning models that directly power the next generation of electric vehicles (EVs). From autonomous driving systems (ADAS) and intelligent battery management to predictive maintenance and personalized in-cabin experiences, machine learning is core to Rivian's mission of keeping adventures safe, efficient, and seamless.

The impact of this role is immense. Unlike traditional software companies where code runs exclusively in the cloud, your models will often run directly on the vehicle's edge hardware. This means your solutions must be highly optimized for latency, compute, and memory constraints. A model you train could optimize energy consumption in real-time, predict a component failure before it happens, or help a vehicle safely navigate challenging off-road terrain.

To succeed, you must be comfortable navigating ambiguity and collaborating across multidisciplinary teams. You will work alongside control systems engineers, hardware designers, and cloud platform teams to transition models from research prototypes to production-grade vehicle software. It is a highly challenging but rewarding environment where your work has a tangible, physical impact on the future of transportation.

The following questions are representative of what you can expect during the Rivian hiring process. These questions are drawn from real candidate experiences and are designed to test your algorithmic problem-solving, deep understanding of machine learning theory, and your ability to design robust systems under real-world constraints.

Coding & Algorithmic Problem Solving

These questions evaluate your core computer science fundamentals, data structure knowledge, and code optimization skills.

• Implement a solution to find the longest path in a directed acyclic graph (DAG) using dynamic programming.
• Write a program to detect cycles in a resource allocation graph for vehicle microservices.

Preparing for a Machine Learning Engineer interview at Rivian requires a balanced approach. You must demonstrate strong software engineering fundamentals, deep machine learning expertise, and a practical understanding of physical system constraints. Your interviewers will look for structured thinking and the ability to articulate complex technical concepts clearly.

Role-Related Knowledge – You must show a deep, first-principles understanding of machine learning algorithms. Do not just explain how to use a framework; be ready to explain the underlying mathematics, optimization techniques, and trade-offs of different architectural choices.

Problem-Solving Ability – Interviewers want to see how you approach ambiguous, open-ended problems. When presented with a system design or algorithmic challenge, start by clarifying requirements, state your assumptions, break down the problem systematically, and evaluate multiple solutions before writing code.

Execution & Engineering Rigor – Rivian values clean, maintainable, and highly optimized code. Whether you are coding in Python or C++, you should write modular, readable code that accounts for edge cases, memory efficiency, and execution speed.

Collaboration & Communication – Because machine learning engineers at Rivian work closely with hardware, product, and operations teams, your ability to explain complex technical decisions to non-experts is critical. Be prepared to discuss your past projects with a focus on ownership, cross-functional collaboration, and business impact.

The interview process for a Machine Learning Engineer at Rivian is rigorous and typically spans three to four weeks. It is designed to evaluate both your theoretical knowledge and your practical execution skills through a mix of coding, system design, and deep technical conversations.

The process begins with an initial technical recruiter screen to align on your background, career interests, and compensation expectations. Following this, you will typically complete a technical screen, which often consists of a coding assessment focusing on algorithms and data structures. Some teams may also include a brief hiring manager conversation at this stage to dive into your resume and assess team fit.

If you pass the initial screens, you will move to the virtual onsite panel. This panel typically consists of four rounds, including coding assessments in Python or C++, multiple technical deep dives into machine learning theory and system design, and a behavioral round with engineering leadership.

The timeline above outlines the standard progression from your initial contact to the final decision. Candidates should use this timeline to pace their preparation, ensuring they allocate sufficient time for coding practice before the technical screen and deep system design preparation before the onsite panel. While the exact duration can vary based on team availability and location, the entire process is designed to move efficiently.

To succeed in the Rivian interview process, you must perform consistently across several core evaluation areas. Below is a detailed breakdown of what to expect in each area and how to prepare.

Coding & Software Engineering

This area evaluates your ability to write clean, production-grade code. While Python is widely used for model development, Rivian also values C++ for on-vehicle, low-latency applications. You should be comfortable writing code in at least one of these languages.

Be ready to go over:

• Data structures and algorithms – Focus on trees, graphs, dynamic programming, and search algorithms.
• Code optimization – Understand how to optimize code for memory usage and execution speed, especially under hardware constraints.
• Language-specific paradigms – Be prepared to explain memory management in C++ or concurrency and vectorization in Python.

Example scenarios:

• "Implement a custom memory-efficient queue to handle incoming high-frequency sensor data streams."
• "Optimize an image preprocessing pipeline to run within a strict millisecond latency budget on an embedded GPU."

Machine Learning System Design

This round tests your ability to design end-to-end machine learning systems that solve real-world automotive and fleet challenges. You must consider data collection, model training, deployment, and monitoring.

Be ready to go over:

• Edge vs. Cloud deployment – Understand when to run models on-vehicle (low latency, offline capability) versus in the cloud (high compute, fleet-wide data access).
• Data pipeline architecture – Design robust pipelines that can ingest, clean, and process massive streams of vehicle telemetry data.
• Model monitoring and retraining – Explain how to detect data drift and safely deploy model updates to a fleet of vehicles.
• Advanced concepts (less common) – Federated learning for privacy-preserving fleet training, active learning for efficient data labeling, and physics-informed neural networks.

Example scenarios:

• "Design a real-time anomaly detection system to monitor battery cell health across a fleet of 100,000 delivery vans."
• "Architect an end-to-end system for collecting, labeling, and training models on rare edge-case scenarios encountered by autonomous vehicles."

Technical Deep Dive & Resume Review

In this round, interviewers will drill down into your past projects. They want to see that you have a deep, hands-on understanding of the systems you built and that you can defend your technical decisions under pressure.

Be ready to go over:

• Model selection trade-offs – Explain why you chose a specific architecture over simpler baselines.
• Metrics and evaluation – Describe how you aligned offline model metrics (e.g., F1-score) with online business or system metrics.
• Failure analysis – Walk through how you debugged a model that failed in production and what guardrails you put in place.

Example scenarios:

• "Walk me through a project where you had to deploy a model under strict latency constraints. What compromises did you have to make?"
• "Describe a scenario where your training data was highly biased. How did you identify the bias, and what steps did you take to mitigate it?"