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Algorithm State Estimation Development Engineer Jobs

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Robotic Algorithm Engineer

Milpitas, CA · On-site

Contribute to state estimation using IMU, joint encoders, and contact sensing Simulation & Tooling ... Contribute to the 01 humanoid platform development * High ownership, fast iteration, engineering ...

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Algorithm State Estimation Development Engineer information

What is the difference between Algorithm State Estimation Development Engineer vs Control Systems Engineer?

AspectAlgorithm State Estimation Development EngineerControl Systems Engineer
Primary FocusDeveloping algorithms for estimating system states from sensor dataDesigning and implementing control strategies for systems
Skills & CertificationsMathematics, signal processing, programming, algorithmsControl theory, systems modeling, programming
Work EnvironmentResearch labs, automotive, robotics, aerospaceManufacturing, automation, robotics, aerospace

Both roles often require strong backgrounds in mathematics and programming, and are used in similar industries like automotive and aerospace. However, Algorithm State Estimation Development Engineers focus on creating algorithms to interpret sensor data, while Control Systems Engineers design control strategies to manage system behavior.

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Robotic Algorithm Engineer

Scylla Solutions LLC

Milpitas, CA

Full-time

Posted 23 days ago

Job description

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