Sr. Staff Hardware Machine Learning/AI Engineer

Role overview

What you will do

• Lead machine learning enablement workflows for Rivian edge AI platforms.
• Build front-end compiler and model-ingestion tooling supporting a broad model zoo on RAP1 and future platforms.
• Develop robust quantization-aware training and model-optimization infrastructure.
• Port, optimize, validate, and benchmark models for hardware accelerators and NPU-class architectures.
• Improve memory, scheduling, sparsity, and data-movement performance with hardware and systems teams.
• Evaluate model fidelity and deployment quality across floating-point and quantized paths.
• Work with software, compiler, architecture, and product teams to accelerate high-value workloads.
• Provide technical leadership and mentoring for engineers in model deployment, compiler infrastructure, and hardware-aware ML.
• Help define the long-term edge AI stack for increasingly large and complex neural network models.

Required qualifications

• Deep experience in machine learning systems, model deployment, or AI infrastructure.
• Strong model-optimization background for edge or accelerated inference, including quantization and performance tuning.
• Experience building or extending compiler, toolchain, or graph-transformation infrastructure for ML workloads.
• Strong understanding of hardware-software co-design for inference acceleration.
• Experience with low-power edge platforms, NPUs, custom accelerators, GPUs, or related architectures.
• Ability to reason about memory allocation, execution scheduling, bandwidth constraints, and system performance.
• Experience translating research concepts into practical tooling and production workflows.
• Strong cross-functional communication and technical-influence leadership skills.

Preferred qualifications

• Experience with quantization-aware training, post-training quantization, compression, pruning, and sparsity optimization.
• Experience bringing vision or multimodal models through full deployment.
• Familiarity with ONNX and model graph transformation pipelines.
• Experience validating numerical behavior across floating-point and integer execution paths.
• Experience supporting external customers, partner enablement, or AI platform productization.
• Background in model-level and hardware-level optimization problems.
• Startup or founder-level experience building end-to-end AI platform capabilities.