Machine Learning Engineer – Simulation Framework

Zoox; Foster City (CA); On-site; USD 100,000 - 140,000; Full time

Job summary

Zoox in Foster City is seeking a Machine Learning Engineer to join their Simulation Core Team. The role focuses on enhancing machine learning efficiency within high-speed simulations to validate safety before real-world deployment.

You'll work on bridging 'sim-to-sim' and 'sim-to-real' fidelity gaps to ensure driving behavior aligns with physical vehicle performance, which is critical for safe operations.

Qualifications

• Strong understanding of machine learning algorithms and techniques.
• Experience with simulation environments and frameworks.
• Ability to work on high-speed GPU systems.

Responsibilities

• Focus on enhancing ML efficiency and fidelity in simulations.
• Drive solutions for simulation-to-reality behavior alignment.
• Collaborate with teams to iterate on driving software.

Skills

Machine Learning

Simulation frameworks

GPU programming

Education

Bachelor's degree in Computer Science or related field

Job description

Simulation is essential for Zoox to rapidly iterate on our driving software and hardware, and to validate our safety before we drive in the real world. We create virtual worlds to challenge our robots, from real-world data, entirely novel scenarios, or a combination of both. Our simulations need to run at a huge scale to cover everything that might happen, and to help prove our driving to be safe.

As a Machine Learning Engineer on the Simulation Core Team, you will focus on the intersection of machine learning and synthetic environments within our high-speed, GPU-based simulation framework. Our success depends on you driving ML efficiency while solving complex "sim-to-sim" and "sim-to-real" fidelity gaps, ensuring our safety-critical models train on data that perfectly aligns with physical vehicle behavior.