Machine Learning Engineer - Simulation Framework

Zoox
Foster City, US

Who this role is best for

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • driving ML efficiency
  • solving complex sim-to-sim and sim-to-real fidelity gaps
Typical background
PhD in Computer Science or related fieldexperience in machine learning

Skills & requirements

Required

Machine LearningSimulationGpu ProgrammingData AlignmentSafety-critical Models

Preferred

Autonomous DrivingRobotics

Stack & domain

Machine LearningSimulation FrameworkGpu-based SimulationMl EfficiencySim-to-simSim-to-realFidelity GapsSafety-critical ModelsData AlignmentDriving SoftwareHardwareSafetyVirtual WorldsRobotics

About the role

Original posting from Zoox via Lever

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.

Source: Zoox careers (Lever)

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