Research Engineer, Robotic Learning

Simulation Engineer

Design simulation environments for NEO (name of robot) and teach NEO (robot) to learn new capabilities via reinforcement learning (RL) algorithms. This enables our robots to be safe and robust in a variety of conditions. Full-stack engineering, from data engineering to model architecture design to shipping polished products. Train NEO to do a diverse set of manipulation and locomotion tasks. Close the sim2real gap between policies trained in simulation and real.

Work with controls, Quality Assurance (QA), and data collection teams to ship reinforcement learning (RL) policies to the production fleet. Deploy skills trained with reinforcement learning (RL) into home environments.

Bachelor's degree or foreign degree equivalent in Computer Science, Mathematics, Software Engineering, Robotics, or related field and four (4) years of experience in Software Engineering or related role.

Experience and/or education must include:

• Train large-scale imitation-learning models for motion planning. Curate expert demonstration datasets, designing loss functions, and deploying policies in real-world or simulated environments. Implement DAgger (Dataset Aggregation), including iteratively collecting additional expert demonstrations in response to model errors, to reduce covariate shift and improve policy robustness.
• Generative architectures such as diffusion models, variational autoencoder, or applied to planning. Model architecture selection and stable training on high-dimensional data.
• Architecting and executing distributed neural-network training across large GPU clusters. Data/model parallelism, gradient synchronization such as NCCL, fault tolerance, and efficient I/O for multi-TB datasets.
• Build and optimize low-latency, real-time software systems. Use of C++ for hard-real-time applications, with hands-on knowledge of multithreading, lock-free data structures, and real-time OS constraints.

Please reference Job  & email resume to careers.ush