Senior Robotics Dynamics & Planning Lead (Humanoid)

Holiday Robotics Inc.; Mountain View (CA); Hybrid; USD 200,000 - 300,000; Full time

Benefits offered by this job

Relocation stipend

Free daily meals

Employer-paid health plan

401(k) retirement plan

Competitive equity

Job summary

Holiday Robotics Research Inc. is seeking a Senior Research Scientist/Engineer to lead dynamics, planning, and control for FRIDAY, the mobile humanoid robot.

You will steer real-time planning, whole-body manipulation, and integration with perception to enable safe navigation in dynamic settings. You will design experiments to identify rigid-body and actuator dynamics, iteratively improve models, and collaborate across hardware, perception, learning, and simulation teams to close the sim-to-real

Qualifications

• BS/MS/PhD in Robotics, ME, EE, CS or related field.
• 7+ years robotics experience centered on dynamics, planning, or control.
• Strong fundamentals in rigid-body mechanics, kinematics, Jacobians, and state estimation.
• Experience turning research into production-quality software on real robots.

Responsibilities

• Lead architecture of real-time planning and control stack for whole-body mobile manipulation.
• Integrate perception and state estimation for collision avoidance and safe navigation.
• Design system identification experiments to characterize dynamics and latency.
• Collaborate with hardware, perception, learning, and simulation researchers; deploy algorithms on FRIDAY.

Skills

Dynamics & Control

Motion Planning

Optimization

State Estimation

Model Predictive Control

Education

BS/MS/PhD in Robotics, ME, EE, CS

Tools

GCS

RRT*

PRM

TrajOpt

MPC

MPPI

Job description

Holiday Robotics Research Inc. is seeking a Senior Research Scientist/Engineer to lead dynamics, planning, and control for FRIDAY, the mobile humanoid robot.

You will steer real-time planning, whole-body manipulation, and integration with perception to enable safe navigation in dynamic settings. You will design experiments to identify rigid-body and actuator dynamics, iteratively improve models, and collaborate across hardware, perception, learning, and simulation teams to close the sim-to-real