AI Training Infrastructure Engineer – Humanoid Whole Body Control
Figure · On-site
MeritLog read this listing from Figure's Greenhouse job board and last checked it on September 9, 2026.
Source: the employer's Greenhouse job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- Conflicting source ranges
- Location
- San Jose, CA
Hiring context
How this role compares at Figure
Figure has 107 live roles in MeritLog’s catalog across 7 job families, and 63 of them are in engineering. 52 of those listings publish a pay range, a disclosure rate of 49%.
Figure concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- Own and scale the infrastructure used to train whole-body control policies (simulation, data pipelines, orchestration, visualizations)
- Design systems that are fast, reliable, and highly configurable for our controls engineers
- Ensure high cluster utilization and minimal downtime-unblocking the team and accelerating iteration cycles
- Evaluate and integrate physics engines, simulation environments, and parameterizations to balance realism and training speed
- Optimize hyperparameters and infrastructure to maximize training speed and efficiency and final model performance
- Build robust tooling to take policies from training → validation → deployment on hardware
What they're asking for
- Strong software engineering fundamentals with production experience in Python and PyTorchSkill
- Experience building or scaling training infrastructure for robotics, control systems, or large-scale ML workloadsSkill
- Familiarity with physics simulation tools such as NVIDIA PhysX, MuJoCo, Warp, or PyBulletSkill
- Working knowledge of dynamics, controls, and robotics systemsSkill
- Experience with reinforcement learning, imitation learning, or policy distillationSkill
- Strong ownership mindset-you own systems that your teammates rely on every daySkill
- Experience modeling contact interactions and photorealistic simulation environments for complex manipulation tasksSkill
- Experience with humanoid or legged robot controlSkillPreferred
- Background in distributed systems, job schedulers, or cluster managementSkillPreferred
- Experience deploying ML models or control policies to real-world systemsSkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
Figure is an AI Robotics company autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It’s time to build. We’re looking for an engineer to own the training and deployment backbone behind our RL-based whole-body control systems. This role sits at the intersection of robotics, machine learning, controls, and software systems engineering, and is critical to how quickly we can iterate, train, and deploy new capability to our fleet of humanoid robots. Key Responsibilities: • Own and scale the infrastructure used to train whole-body control policies (simulation, data pipelines, orchestration, visualizations) • Design systems that are fast, reliable, and highly configurable for our controls engineers • Ensure high cluster utilization and minimal downtime-unblocking the team and accelerating iteration cycles • Evaluate and integrate physics engines, simulation environments, and parameterizations to balance realism and training speed • Optimize hyperparameters and infrastructure to maximize training speed and efficiency and final model performance • Build robust tooling to take policies from training → validation → deployment on hardware Requirements: • Strong software engineering fundamentals with production experience in Python and PyTorch • Experience building or scaling training infrastructure for robotics, control systems, or large-scale ML workloads • Familiarity with physics simulation tools such as NVIDIA PhysX, MuJoCo, Warp, or PyBullet • Working knowledge of dynamics, controls, and robotics systems • Experience with reinforcement learning, imitation learning, or policy distillation • Strong ownership mindset-you own systems that your teammates rely on every day • Experience modeling contact interactions and photorealistic simulation environments for complex manipulation tasks Bonus Qualifications: • Experience with humanoid or legged robot control • Background in distributed systems, job schedulers, or cluster management • Experience deploying ML models or control policies to real-world systems The US base salary range for this full-time position is between $150,000 and $300,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.