Simulation Software Engineer – Scale Robot Learning (Equity)

1X; San Carlos (CA); On-site; USD 200,000 - 280,000; Full time

Benefits offered by this job

Comprehensive medical, dental, and vis

Generous paid time off

401(k) plan with company match

FSA/HSA options

Commuter benefits

Disability and life insurance

Employee Assistance Program (EAP)

Onsite snacks and catered lunches

Job summary

1X in San Carlos, CA is building simulation environments and real-time infrastructure to accelerate robot learning. You will own the environments and systems that enable the AI team to iterate safely and at scale.

The role focuses on narrowing the sim-to-real gap, enhancing fidelity, and scaling synthetic data production to support rapid policy development and hardware prototyping in a high-throughput training setup.

Qualifications

• 4+ years of experience programming in Python, C++, or similar languages with experience building environments or benchmarks using robotics simulators (MuJoCo, PyBullet, Isaac Sim, or equivalent)
• Experience improving the performance of physics simulators or OpenGL rendering pipelines
• Strong testing practices for simulation stacks used in robot learning—comfortable writing tests that verify physical correctness and catch regressions
• Advanced degree (MS or PhD) in Computer Science or a related field

Responsibilities

• Build and maintain the simulation environments and real-time infrastructure that accelerate robot learning at 1X.
• Reduce dependence on physical robot evaluations and scale synthetic data production.
• Ensure policies trained in simulation transfer reliably to real hardware and maintain high-throughput training data.

Skills

Python

C++

Robotics simulators

Testing practices

OpenGL

Performance optimization

Education

MS/PhD in Computer Science

Tools

MuJoCo

PyBullet

Isaac Sim

Warp

JAX

Job description

1X in San Carlos, CA is building simulation environments and real-time infrastructure to accelerate robot learning. You will own the environments and systems that enable the AI team to iterate safely and at scale.

The role focuses on narrowing the sim-to-real gap, enhancing fidelity, and scaling synthetic data production to support rapid policy development and hardware prototyping in a high-throughput training setup.