Humanoid Robotics Engineer - Whole-Body RL & Control

SERES; Milpitas (CA); On-site; USD 80,000 - 120,000; Full time

Job summary

SERES is building a next-generation humanoid robot platform and seeks a Robotics Algorithm Engineer to advance whole-body control, learning-based policies, and perception for real-world deployment. You will work across VLA/WAM, vision-based RL, simulation, state estimation, and deployment to coordinate locomotion, manipulation, and interaction.

You will implement robust control, integrate with WBC and IK, and develop policies that transfer from sim to real hardware.

Qualifications

• 3+ years of robotics, controls, reinforcement learning, or related fields.
• Strong C++ and Python skills.
• Experience developing learning-based robot control policies.
• Experience deploying algorithms on real robots.
• Experience with whole-body control, humanoid robotics, legged robotics, or mobile manipulation.
• Hands-on experience with reinforcement learning and/or imitation learning.
• Solid understanding of rigid-body dynamics, floating-base systems, contact dynamics, and feedback control.
• Experience with MuJoCo, Isaac Sim / Isaac Lab, or similar simulation platforms.
• Familiarity with state estimation and multimodal sensing.
• Strong simulation, algorithm, and hardware debugging skills.
• Experience working in Linux environments.

Responsibilities

• Develop and deploy learning-based whole-body control policies for humanoid robots.
• Coordinate locomotion, balance, torso, arms, and end-effectors.
• Integrate learned policies with WBC, inverse dynamics, IK, and optimization-based control.
• Develop robust contact-aware behaviors for locomotion, manipulation, and interaction.
• Analyze and debug instability, contact failures, coordination issues, and policy failures.
• Develop and integrate VLA / WAM models for humanoid control.
• Adapt foundation-model-based policies to humanoid embodiment and full-body action spaces.
• Connect high-level semantic reasoning with low-level whole-body control.
• Design action spaces, observations, policy interfaces, and skill representations.
• Explore imitation learning, behavior cloning, diffusion policies, transformers, and RL.
• Use teleoperation, demonstration, and robot interaction data for training and fine-tuning.
• Build humanoid simulation and training environments using MuJoCo, Isaac Sim / Isaac Lab, or similar platforms.
• Develop scalable RL, imitation learning, and visuomotor training pipelines.
• Design tasks, curricula, rewards, domain randomization, and system identification.
• Generate and use simulation, teleoperation, demonstration, and real-robot datasets.
• Analyze sim-to-real gaps in dynamics, contact, sensing, perception, and actuators.
• Deploy whole-body and visuomotor policies on humanoid hardware with high-bandwidth torque control.
• Perform real-robot tuning, debugging, system identification, and optimization.
• Diagnose failures across perception, policy inference, estimation, dynamics, latency, and low-level control.
• Optimize policy inference and control pipelines for real-time execution.
• Work closely with perception, firmware, motor control, systems, and hardware teams.

Skills

C++

Python

Reinforcement learning

Robot control

Imitation learning

MuJoCo

Isaac Sim

Tools

MuJoCo

Isaac Sim

Isaac Lab

Job description

SERES is building a next-generation humanoid robot platform and seeks a Robotics Algorithm Engineer to advance whole-body control, learning-based policies, and perception for real-world deployment. You will work across VLA/WAM, vision-based RL, simulation, state estimation, and deployment to coordinate locomotion, manipulation, and interaction.

You will implement robust control, integrate with WBC and IK, and develop policies that transfer from sim to real hardware.