Research Engineer, 1X

• Bachelor’s degree in Computer Science or equivalent
• 3+ years of professional, full-time experience building simulation
• Experience with physical simulators (Pybullet, IssacSim, Mujoco, etc.) and training policies in simulation
• Experience with physics simulation (contact dynamics, system identification, physics engines)
• Experience learning-based approaches to close the sim-to-real gap (domain adaptation, randomization)
• Experience generating simulated assets and environments (Blender, Maya)
• Ability to rapidly prototype ideas in code in an independent manner
• Published research in top Simulation or ML conferences (SIGGRAPH, NeurIPS, CoRL, RSS, etc.)

Desirable

• Research Engineers in simulation are generalists who build the simulation stack and close the sim-to-real gap
• A typical day involves building contact-rich physics simulation, scaling up reinforcement learning algorithms, or solving the sim-to-real gap in the visual or physics domains
• You will own simulation and focus on making simulation as widely used as possible within the company
• By making simulation a trusted proxy of the real world, you will drive AI model evaluation and iteration speed
• You will work closely with other AI team members and also develop independently
• Your role on the team will be to build ML robotics simulation and close the sim-to-real gap
• You will work across the AI stack and solve gaps in model training and inference
• We aim to (1) double the number of tasks the robot can perform and (2) half the error rate on existing tasks, quarter over quarter
• The team works closely together to prioritize direction and scale up a single approach to a production-ready system
• Day to day: ship AI stack for non-technical robot operators, scale up AI model training workloads, and own model inference reliability
• Bring real-world tasks and projects into simulation via real-to-sim
• Build the data engine (frontend UI and backend) to log, clean, and label data for foundation model training
• Work with our robot operations team to scale up our datasets and model capabilities

• Introduction Stage - we have initial conversations to get to know you better: [30m] Recruiter Screen with Win Yu and [45m] Hiring Manager Screen with a AI team
• Team Interview Stage - we then dive into your experience in more depth and introduce you to members of the team. This is also the stage where you will do your take-home challenge: [90m] Technical Deep-Dive conversation with the AI team
• Final Interview Stage - we move you to our final round: [15m] Role expectation management with Eric Jang and [30m] Conversation with CEO Bernt Øivind Børnich