Staff Machine Learning Infrastructure Engineer (Simulation)

• BS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience
• 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model
• Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a huge plus
• Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with custom-kernels for diverse h/w compute based efficiency
• Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks
• MS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience
• 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model
• Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks

Key responsibilities

• Lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations.
• Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training.
• Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models, and mentor junior engineers.