Robotics Engineer

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.