Robotic Algorithm Engineer
Scylla Solutions, Milpitas, CA, United States
About the job Robotic Algorithm Engineer
We are building a next-generation humanoid robot platform with high-bandwidth torque-controlled joints and full-body actuation. Our short- to mid-term goal is to achieve robust and reliable locomotion in indoor service and industrial environments.
As a
Robotics Algorithm Engineer
focused on Locomotion, you will work across simulation, learning-based control, state estimation, and real-robot deployment. This is a highly hands-on role requiring both strong implementation skills and the ability to debug complex real-world robotic behaviors.
We are looking for engineers who not only implement algorithms, but also develop their own technical insights and adapt quickly in a rapidly evolving robotics landscape.
Responsibilities
Locomotion & Learning-Based Control
Develop and deploy RL-based locomotion policies for humanoid robots
Design training pipelines including domain randomization and sim-to-real transfer
Improve policy robustness for indoor service and industrial use cases
Analyze and debug failure modes from both simulation and real-world testing
Full-Body Control & Modeling
Work on whole-body control frameworks integrating learned policies
Understand and leverage robot dynamics models for stability and contact reasoning
Contribute to state estimation using IMU, joint encoders, and contact sensing
Simulation & Tooling
Build and maintain locomotion simulation environments (Mujoco / Isaac)
Design training environments and reward shaping strategies
Analyze the simulation-real gap and iterate on mitigation strategies
Real Robot Deployment
Deploy policies to hardware with torque-controlled, high-bandwidth actuators
Perform real-robot tuning, debugging, and performance optimization
Work closely with firmware, motor control, and hardware teams
Qualifications
Must Have
3+ years of experience in robotics, control, or locomotion-related roles
Strong C++ and Python programming skills
Experience applying reinforcement learning to robotics control problems
Experience deploying algorithms on real robots (not simulation-only)
Solid understanding of rigid body dynamics and feedback control
Familiarity with state estimation for legged robots
Experience working in Linux environments
Nice to Have
Experience with humanoid or legged robots
Whole-Body Control or MPC exposure
Mujoco / Isaac Gym / Isaac Sim experience
Experience addressing sim-to-real transfer challenges
Familiarity with Pinocchio, CasADi, or similar tools
CUDA or large-scale RL training experience
ROS2 experience
Work Mode
On-site required
Up to 10% of travel
We are building a next-generation humanoid robot platform with high-bandwidth torque-controlled joints and full-body actuation. Our short- to mid-term goal is to achieve robust and reliable locomotion in indoor service and industrial environments.
As a
Robotics Algorithm Engineer
focused on Locomotion, you will work across simulation, learning-based control, state estimation, and real-robot deployment. This is a highly hands-on role requiring both strong implementation skills and the ability to debug complex real-world robotic behaviors.
We are looking for engineers who not only implement algorithms, but also develop their own technical insights and adapt quickly in a rapidly evolving robotics landscape.
Responsibilities
Locomotion & Learning-Based Control
Develop and deploy RL-based locomotion policies for humanoid robots
Design training pipelines including domain randomization and sim-to-real transfer
Improve policy robustness for indoor service and industrial use cases
Analyze and debug failure modes from both simulation and real-world testing
Full-Body Control & Modeling
Work on whole-body control frameworks integrating learned policies
Understand and leverage robot dynamics models for stability and contact reasoning
Contribute to state estimation using IMU, joint encoders, and contact sensing
Simulation & Tooling
Build and maintain locomotion simulation environments (Mujoco / Isaac)
Design training environments and reward shaping strategies
Analyze the simulation-real gap and iterate on mitigation strategies
Real Robot Deployment
Deploy policies to hardware with torque-controlled, high-bandwidth actuators
Perform real-robot tuning, debugging, and performance optimization
Work closely with firmware, motor control, and hardware teams
Qualifications
Must Have
3+ years of experience in robotics, control, or locomotion-related roles
Strong C++ and Python programming skills
Experience applying reinforcement learning to robotics control problems
Experience deploying algorithms on real robots (not simulation-only)
Solid understanding of rigid body dynamics and feedback control
Familiarity with state estimation for legged robots
Experience working in Linux environments
Nice to Have
Experience with humanoid or legged robots
Whole-Body Control or MPC exposure
Mujoco / Isaac Gym / Isaac Sim experience
Experience addressing sim-to-real transfer challenges
Familiarity with Pinocchio, CasADi, or similar tools
CUDA or large-scale RL training experience
ROS2 experience
Work Mode
On-site required
Up to 10% of travel