Berkshire Grey ML Engineer one candidate reports a recruiter screen, hiring-manager video call, then an onsite presentation and four technical one-on-ones spanning ML, robotics, coding, RL, simulation, and sim2real.
$136K
Avg. Base Comp
$158K
Avg. Total Comp
7 rounds
Typical Rounds
Not reported
Process Length
For a Berkshire Grey ML Engineer interview, prepare for a process that connects general machine-learning ability with robotics-specific judgment. One candidate reported an initial 30-minute recruiter conversation, followed by a conversational hiring-manager video interview centered on their resume plus ML and robotics questions. The reported onsite was the most substantial stage: a presentation came first, followed by four one-on-one conversations with team members.
The onsite covered both breadth and applied reasoning. The candidate encountered basic ML knowledge, recent advances in ML for robotics, robotics concepts, coding, reinforcement learning, simulation platforms, and sim2real transfer. Rather than treating those as isolated study areas, be ready to explain how choices in one area affect a robotics project: what problem you were solving, why you selected an approach, how you evaluated it, and what you changed when results were imperfect.
The presentation and project discussions make concrete examples especially valuable. Select work you can explain end to end, including your personal decisions and tradeoffs, then practice moving between the technical detail and the practical objective. Berkshire Grey builds AI-enabled robotics for warehouse automation, so framing experience around deploying or validating ML in a physical-systems setting may be relevant. This guide reflects one detailed candidate account, so exact sequencing and emphasis may vary.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Berkshire Grey process.
A recruiter contacted the candidate on LinkedIn for a 30-minute call about their background and the position. Next came a video interview with the hiring manager, combining resume discussion with machine-learning and robotics questions. The onsite began with a presentation and continued with four one-on-one interviews. Topics included ML fundamentals, recent ML-for-robotics advances, robotics concepts, coding, reinforcement learning, simulation platforms, sim2real transfer, and project-specific questions about problem-solving and decisions. The candidate was later told the team liked their experience, expertise, and personality, but was ultimately rejected after the final candidate was interviewed.
Prep tip from this candidate
Prepare to cover technical breadth in the onsite: candidates report dedicated discussion of ML fundamentals, ML for robotics, reinforcement learning, simulation platforms, and sim2real transfer. Prepare a clear project presentation and practice explaining the decisions and reasoning behind your past work.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Berkshire Grey
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Compute Deviation | |
| Permutation Palindrome | |
| Bagging vs Boosting | |
| P-value to a Layman | |
| Prime to N | |
| Get Top N Frequent Words | |
| Compute Variance | |
| Nearest Common Ancestor | |
| Recurring Character | |
| Bank Fraud Model | |
| Random Forest Explanation | |
| Jars and Coins | |
| Type-ahead Search | |
| Encoding Categorical Features | |
| Weekly Aggregation | |
| Same Algorithm Different Success | |
| Missing Housing Data | |
| Flatten JSON | |
| Hurdles In Data Projects | |
| Find the First Non-Repeating Character in a String | |
| Valid Anagram | |
| Booking Regression | |
| RMS Error | |
| Biased Random Number Generator | |
| Lasso vs Ridge | |
| Dice Worth Rolling | |
| Priority Queue Using Linked List | |
| Variable Error | |
| Assumptions of Linear Regression |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports being contacted on LinkedIn for a 30-minute recruiter call covering their background and the ML Engineer position. Prepare a concise account of relevant experience and the kind of robotics or ML work you want to pursue.
The reported video interview was conversational and focused mainly on the candidate's resume, with several machine-learning and robotics questions. Be ready to connect past work to the role and explain how you approached technical problems.
The onsite reportedly opened with a presentation. Choose a project you can narrate clearly: problem, approach, key decisions, evaluation, and what you would improve. Candidates may be asked to defend the reasoning behind their choices.
One candidate completed four one-on-one interviews covering ML fundamentals, ML-for-robotics developments, robotics concepts, coding, reinforcement learning, simulation platforms, and sim2real transfer. Expect breadth, then prepare to go deeper where your own projects provide evidence.
