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?"
