Senior Robotics Engineer at Scylla Solutions Milpitas, CA

Position Summary

The Senior Robotics Engineer will lead the design, development, and implementation of advanced locomotion, navigation, control, and machine learning systems for next-generation robotic platforms. This role workspace requires deep technical expertise in robotic motion control, dynamic stability, and applied machine learning for perception and control optimization.

The successful candidate will take ownership of complex robotic systems from concept to deployment, combining classical control theory with data-driven methods to enable adaptive, efficient, and intelligent mobility in real-world environments.

• Lead the design and development of robotic systems focused on lower‑body locomotion, including bipedal walking, balance control, and terrain adaptation.
• Architect and implement advanced control and motion planning algorithms leveraging both model‑based and machine learning‑based approaches.
• Develop and integrate স্থাপন navigation systems using sensor fusion, SLAM, and path planning for autonomous mobility.
• Apply machine learning algorithms—including reinforcement learning, imitation learning, or supervised learning—for motion optimization, adaptive control, and perception‑driven decision‑making.
• Conduct simulation, modeling, and real‑world validation of locomotion and control systems under dynamic conditions.
• Collaborate with mechanical, electrical, and software teams to ensure seamless integration and system performance.
• Mentor junior engineers and contribute to cross‑functional design reviews and project leadership.
• Maintain detailed documentation of designs, experiments, and results.
• Stay current with advancements in robotics, control theory, and machine learning, and drive continuous innovation.

• Masters or Ph.D. degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related discipline.
• 5+ years of hands‑on experience in robotic locomotion, control systems, or humanoid robotics (academic or industry).
• Proven experience applying machine learning algorithms (reinforcement learning, neural networks, or adaptive control) to robotics or control applications.
• Strong foundation in control theory, kinematics, dynamics, and trajectory optimization.
• Pro peru noun in programming languages such as C++, Python, and MATLAB/Simulink.
• Experience developing and tuning real‑time control systems for actuated robotic mechanisms.
• Expertise with ROS/ROS2 and simulation tools such as Gazebo, Mu Jo Co , or Isaac Sim.
• Experience integrating sensors such as IMUs, vision systems, and force/torque sensors.
• Excellent analytical, problem‑solving, and communication skills.
• Proven ability to manage complex technical projects and work effectively in cross‑functional environments.

• Advanced experience applying reinforcement learning and model predictive control (MPC) for motion control and planning.
• Background in mechanical, biomechanics, prosthetics, or exoskeleton design.
• Prior work on humanoidatterson or legged robotic systems in R&D or product environments.
Track record of publications, patents, or open-source contributions in robotics едіْ, or control assembly.
• Experience leading specific engineering teams or mentoring in a research or product development context.

• Position based in Milpitas, CA, with occasional travel to partner sites or testing facilities as required.
• Work is primarily conducted in a laboratory and office environment, involving both simulation and physical prototyping.