What is a Machine Learning Engineer at Berkshire Grey?

As a Machine Learning Engineer at Berkshire Grey, you are at the forefront of transforming the global supply chain through intelligent robotic automation. Your work directly enables fleets of robots to perceive, decide, and act in highly dynamic, unstructured physical environments. Unlike traditional software roles, your models will not just live on a server—they will drive the physical movements of robotic arms, sorting systems, and mobile robots that handle millions of items daily for massive retail, eCommerce, and logistics enterprises.

This position sits at the critical intersection of artificial intelligence and physical engineering. You will be tackling complex challenges in computer vision, reinforcement learning, and robotic manipulation. The impact of your work is immediate and visible; a millisecond improvement in inference time or a slight increase in grasp success rates translates directly to massive operational efficiencies for our customers.

Because Berkshire Grey is actively expanding its R&D capabilities, this role requires both deep technical rigor and an entrepreneurial mindset. You will not just be tuning existing pipelines; you will be conceptualizing, building, and deploying novel ML solutions to solve industry problems that have never been solved before. Expect a collaborative, fast-paced environment where your expertise will shape the future of autonomous robotic picking and logistics.

Common Interview Questions

Getting Ready for Your Interviews

To succeed in our interview process, you must demonstrate a balance of theoretical knowledge, practical engineering skills, and a deep understanding of how software interacts with the physical world.

Technical and Domain Expertise – We evaluate your foundational knowledge in machine learning, computer vision, and robotics. You must show a strong grasp of the math and theory behind the algorithms you use, as well as hands-on experience with relevant frameworks and simulation platforms.

Applied Problem-Solving – Interviewers will test your ability to apply ML principles to real-world, scenario-based industry problems. We look for candidates who can take an ambiguous warehouse automation challenge, break it down, and design a robust, scalable machine learning solution.

Engineering Rigor – A great model is useless if it cannot run reliably in production. We assess your ability to write clean, efficient Python code and your understanding of how to deploy ML models in resource-constrained or real-time environments.

Communication and Culture Fit – Because our R&D teams are highly cross-functional, you must be able to communicate complex ML concepts to hardware engineers, product managers, and other stakeholders. We value adaptability, curiosity, and a collaborative approach to solving hard problems.

Interview Process Overview

The interview process for a Machine Learning Engineer at Berkshire Grey is designed to be thorough, engaging, and highly relevant to the day-to-day work. Your journey typically begins with a recruiter phone screen to discuss your background, interests, and alignment with our current R&D initiatives. If there is a mutual fit, you will move on to a video interview with the hiring manager. This conversation dives deeper into your resume and touches on foundational machine learning and robotics concepts. Depending on the specific team, you may also have an additional technical screen with a Principal Engineer.

The final stage is an intensive, half-day onsite (or virtual onsite) interview. A unique and critical component of this stage is a technical presentation prepared and delivered by you, detailing a past project. This is followed by four to five back-to-back 1:1 interviews with various team members. These sessions are structured to assess both your breadth across ML disciplines and your depth in specific areas like computer vision, reinforcement learning, and software engineering.

Our interviewers strive to create a comfortable, conversational environment. We are not looking to trick you; rather, we want to see how you think, how you handle complex scenario-based questions, and how you would collaborate with our team to push the boundaries of robotic automation.

This timeline illustrates the progression from initial screening to the final onsite loop. Use this visual to structure your preparation, ensuring you allocate dedicated time to refine your technical presentation and review foundational ML and computer vision concepts before the final rounds.

Deep Dive into Evaluation Areas

Your onsite interviews will feature distinct modules, each focusing on a different technical theme. Understanding these core evaluation areas will help you focus your preparation effectively.

Machine Learning and Computer Vision Fundamentals

At Berkshire Grey, perception is everything. Robots need to understand what they are looking at before they can manipulate it. This area tests your grasp of the core concepts that power our robotic vision systems. You need to demonstrate that you understand not just how to use a library, but how the underlying algorithms function.

Be ready to go over:

• 2D and 3D Computer Vision – Object detection, instance segmentation, pose estimation, and working with point clouds or depth sensors (RGB-D).
• Model Architecture and Training – CNNs, Transformers, loss functions, optimization algorithms, and strategies for handling imbalanced datasets.
• Performance Trade-offs – Balancing model accuracy with inference speed, which is critical for real-time robotic operations.
• Advanced concepts (less common) – Multi-modal sensor fusion, self-supervised learning, and domain adaptation from simulation to reality (Sim2Real).

Example questions or scenarios:

• "How would you design a vision system to identify and segment transparent or highly reflective objects in a cluttered bin?"
• "Explain the mathematical differences between various loss functions used in object detection."
• "Walk me through how you would optimize a deep learning model to run inference in under 50 milliseconds on edge hardware."

Robotics, Reinforcement Learning, and Simulation

Because our ML models interact with physical hardware, you must understand the robotics context. Even if your background is purely ML, you need to show an aptitude for how models integrate with kinematics, motion planning, and control systems.

Be ready to go over:

• Reinforcement Learning (RL) – Markov Decision Processes, Q-learning, policy gradients, and reward shaping for robotic tasks.
• Simulation Platforms – Experience with tools like Isaac Sim, PyBullet, or MuJoCo, and how to use them to generate synthetic training data.
• Grasping and Manipulation – Approaches to predicting grasp poses and planning collision-free trajectories.
• Advanced concepts (less common) – Inverse kinematics, ROS (Robot Operating System) integration, and continuous control algorithms.

Example questions or scenarios:

• "How would you set up a simulation environment to train a robotic arm to pick up objects of varying weights and textures?"
• "Discuss the challenges of transferring a reinforcement learning policy trained in simulation to a physical robot."
• "What reward structure would you design to teach a robot to untangle overlapping items in a sorting bin?"

Software Engineering and Python Coding

Research code is not production code. We need engineers who can write robust, scalable software. You will face a dedicated coding round focused on your ability to write clean, idiomatic Python code.

Be ready to go over:

• Data Structures and Algorithms – Standard algorithmic problem-solving, focusing on efficiency and optimal time/space complexity.
• Python Proficiency – Object-oriented programming, standard libraries, and writing modular, testable code.
• ML Frameworks – Deep practical knowledge of PyTorch or TensorFlow, including custom data loaders and distributed training setups.
• Advanced concepts (less common) – C++ integration, CUDA programming, and optimizing data pipelines for high-throughput training.

Example questions or scenarios:

• "Write a Python function to process and filter a stream of noisy sensor data in real-time."
• "Design a class structure to manage the state of a multi-robot sorting system."
• "Implement an algorithm to find the optimal path for a robot navigating a grid with dynamic obstacles."

Scenario-Based Industry Problem Solving

This is where theory meets reality. Interviewers will present you with open-ended, real-world challenges faced by Berkshire Grey. We are evaluating your system-level thinking, your ability to handle ambiguity, and your pragmatic approach to engineering.

Be ready to go over:

• End-to-End System Design – Architecting an ML pipeline from data collection and annotation to model deployment and monitoring.
• Failure Modes and Edge Cases – Identifying where a system might fail in a physical warehouse environment and designing fallbacks.
• Iterative Development – How you build a baseline model quickly and improve it over time based on real-world metrics.

Example questions or scenarios:

• "Our robotic picking system is suddenly dropping 10% of items. Walk me through how you would debug this issue from the ML side."
• "Design a data collection strategy for a new customer whose inventory changes completely every season."
• "How would you approach building a perception system for a warehouse that has highly variable lighting conditions?"