Staff Machine Learning Engineer, Infrastructure

Join our ML Infrastructure engineering team advancing state-of-the-art ultra-realistic multi-agent simulations using foundation models. In this role, you will work at the intersection of ML infrastructure, foundation models, and simulation engineering, with a specific focus on writing high-performance business and simulation logic in JAX/TensorFlow running directly on TPUs to power realistic environments for Reinforcement Learning (RL).

What You'll Do

• Design, build, and optimize realistic simulation environments and business logic running on TPUs using JAX and TensorFlow. Implement and optimize large-scale model and data parallelism strategies for training and running foundation models on TPU hardware.
• Collaborate closely with modeling teams to integrate foundation models into simulation pipelines.
• Drive technical architectures and system designs from data engineering through simulation execution to meet business and performance objectives.
• Profile systems, identify performance bottlenecks across ML accelerators, and optimize end-to-end execution speed.
• Translate product and business goals into concrete technical requirements and system deliverables.

Minimum Qualifications

• 6+ years of professional software engineering experience, with at least 4 years focused on machine learning infrastructure (scaling, training, optimizing, and deploying large-scale ML systems).
• Direct ML programming experience on TPU and GPU hardware using frameworks such as JAX, PyTorch, or TensorFlow.
• Proven hands-on experience scaling large models using model parallelism, data parallelism, or distributed training techniques.
• Strong understanding of state-of-the-art ML models (e.g., autoregressive transformers) and hands-on proficiency with ML accelerator profiling tools to diagnose bottlenecks.
• Demonstrated ability to independently lead ambiguous technical initiatives end-to-end and build robust libraries, pipelines, and developer tooling.
• Strong verbal and written communication skills to collaborate effectively across distributed, cross-functional teams.

Preferred Qualifications

• Practical experience in Reinforcement Learning (RL), Sim2Real transfer, or Robotics.
• Experience with distributed ML frameworks and accelerators like GPU/TPU.
• Domain familiarity with Autonomous Driving systems, multi-agent simulations, or realistic world modeling.