Robotics Engineer

SERES•Milpitas, CA

2d•$80,000 - $120,000

About The Position

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.

Requirements

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

Nice To Haves

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
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

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
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
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
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