Senior ML Engineer — Whole-Body Control & Simulation

Pantera Capital; San Francisco (CA); On-site; USD 200,000 - 350,000; Full time

Benefits offered by this job

Comprehensive benefits package including medical, dental, and vision coverage

Access to a 401(k) plan

Equity through the company's discretionary equity program

Job summary

A technology-focused company in San Francisco seeks candidates for a role specializing in robotic control systems. You will train whole-body policies, build simulation environments, and run GPU training experiments. Ideal candidates should have strong coding skills in Python, C++, or Rust, and a solid understanding of reinforcement learning principles. The position offers a competitive compensation range of $200K to $350K, and you’ll work with a small, elite team in a dynamic, high-performance environment.

Qualifications

• Very strong coding skills in Python, C++, or Rust. CUDA is a plus.
• Strong foundations in modern reinforcement learning.
• Experience training embodied control policies.
• Hands-on robotics simulation experience.
• Solid intuition for dynamics, contact, and actuation.
• Comfortable running large-scale GPU experiments.

Responsibilities

• Train whole-body policies for locomotion, manipulation, and coordinated motion.
• Build scalable simulation environments in IsaacLab, MuJoCo, or similar.
• Design rewards and curricula for stable long-horizon learning.
• Own sim-to-real transfer using domain randomization.
• Run and debug large-scale GPU training experiments.

Skills

Strong coding skills in Python

Strong coding skills in C++

Strong coding skills in Rust

Experience in CUDA

Foundations in modern reinforcement learning

Robotics simulation experience

Intuition for dynamics, contact, and actuation

Experience with large-scale GPU experiments

Job description

A technology-focused company in San Francisco seeks candidates for a role specializing in robotic control systems. You will train whole-body policies, build simulation environments, and run GPU training experiments. Ideal candidates should have strong coding skills in Python, C++, or Rust, and a solid understanding of reinforcement learning principles. The position offers a competitive compensation range of $200K to $350K, and you’ll work with a small, elite team in a dynamic, high-performance environment.