Robotics Engineer

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

About the Role

We are building a next-generation humanoid robot platform with high-bandwidth torque-controlled joints and full-body actuation.

As a Robotics Algorithm Engineer focused on Humanoid Whole-Body Control , you will work across VLA / WAM, vision-based RL, whole-body control, simulation, state estimation, and real-robot deployment. You will develop learning-based systems that coordinate locomotion, manipulation, perception, and full-body motion.

We are looking for engineers with strong implementation skills, solid robotics fundamentals, and the ability to turn research ideas into reliable real-world robot behaviors.

Responsibilities

Whole-Body Learning & Control

• 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

VLA / WAM & Generalist Policies

• 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

Vision-Based Reinforcement Learning

• Develop vision-based RL policies using RGB, depth, proprioception, and onboard sensing
• Build visuomotor policies for locomotion, navigation, mobile manipulation, and whole-body tasks
• Develop visual-proprioceptive representation learning and sensor fusion
• Use privileged learning, teacher‑student training, distillation, domain randomization, and sim-to-real
• Improve robustness to environment variation, object variation, appearance changes, occlusion, and sensor noise

Modeling, State Estimation & Control

• Apply rigid-body dynamics, contact dynamics, and humanoid kinematics to whole-body control
• Develop and integrate state estimation using IMU, encoders, force/contact sensing, and vision
• Work with floating‑base dynamics and multi‑contact estimation
• Combine learning‑based policies with feedback control and model‑based methods

Simulation, Data & Training

• 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

Real Robot Deployment

• 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

Qualifications

Must Have

• 3+ years of experience in robotics, controls, reinforcement learning, imitation 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

Strongly Preferred

• Experience with VLA, WAM, whole‑body action models, or generalist robot policies
• Experience with vision‑based RL or visuomotor learning
• Experience with transformer-based policies, diffusion policies, behavior cloning, or large‑scale imitation learning
• Experience combining RGB / RGB‑D observations with proprioception
• Experience with humanoid locomotion and manipulation
• Experience with domain randomization, privileged learning, distillation, and system identification
• Experience with teleoperation and demonstration‑data pipelines
• Experience with real‑time policy deployment and GPU inference
• Experience integrating learned policies with WBC, MPC, inverse dynamics, IK, or trajectory optimization

Nice to Have

• Publications or strong project experience in humanoid robotics, robot learning, RL, imitation learning, VLA, or visuomotor control
• Experience with large‑scale robot datasets and multi‑task policy training
• Experience with dexterous or bimanual manipulation
• Familiarity with foundation models for robotics and embodied AI
• Experience with object detection, tracking, 3D perception, or scene representations
• Experience building production‑quality robotics software and deployment infrastructure

Pay Range: $80,000- $120,000 per year. The actual base salary offered will depend on factors such as the candidate’s experience, skills, qualifications, and job-related considerations. This position may also be eligible for additional compensation and benefits.

SERES is an equal opportunity employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.