Reinforcement learning engineer

About the role

Physical AI capabilities expand through reinforcement learning algorithms that shape robot behavior. The team lowers barriers for builders so research ideas move beyond prototype stages into production systems. This role designs and deploys methods that teach robots diverse tasks.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Robot capabilities grow as reinforcement learning methods are designed and implemented for varied tasks. Robust training pipelines form when algorithms are developed and optimized for both simulation and real-world testing. Reliable model integration happens as robotics engineers collaborate on production deployments. Systematic algorithm performance improvements emerge from structured experiments that measure outcomes. Efficient learning across multiple robots becomes possible when training infrastructure scales to meet fleet demands.

Requirements

Reinforcement learning experience must cover methods such as PPO, SAC, TD3, and DDPG. Hands-on work with robotics systems is required through simulation or real robots in practice. A record of applying RL to manipulation, locomotion, or navigation tasks must be evident in prior work. Python proficiency guides the use of deep learning frameworks including PyTorch, TensorFlow, and JAX for model development. Understanding of robot kinematics, dynamics, and control informs practical solutions to real problems. Experience with GPU-based simulation such as Isaac Gym, Isaac Lab, and SAPIEN supports demanding training workflows.

Nice to have

Distributed RL training experience helps coordinate learning across many robots in complex environments. Sim-to-real transfer techniques reduce gaps between virtual training and physical robot behavior. Publications in venues such as CoRL, ICRA, RSS, NeurIPS, ICLR, and ICML signal research rigor and peer recognition.

Practical notes

Work is conducted from the Fremont Office with full_time engagement. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Reinforcement learning trains agents to make sequences of decisions through interaction with environments. Simulation platforms allow safe testing of policies before real hardware deployment. Frameworks for physical AI combine software control with mechanical systems to expand what robots can do.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.