Sr. Staff Hardware Machine Learning/AI Engineer

About Rivian

Role summary

Responsibilities

• Lead machine-learning enablement workflows for edge AI platforms.
• Build compiler front-end and model-ingestion tooling to support models on RAP1 and future platforms.
• Develop quantization-aware training and model-optimization infrastructure.
• Port, optimize, validate, and benchmark models on accelerators and NPU-class architectures.
• Improve performance across memory, scheduling, sparsity, and data movement with hardware and systems teams.
• Evaluate model fidelity and deployment quality across floating-point and quantized paths, including accuracy, error, and performance trade-offs.
• Coordinate with software, compiler, architecture, and product teams to prioritize valuable workloads.
• Provide technical leadership and mentoring across model deployment, compiler infrastructure, and hardware-aware ML.
• Help set the long-term direction of the edge AI stack for increasingly large and complex neural networks.

Required qualifications

• Deep experience in machine-learning systems, model deployment, or AI infrastructure.
• Strong edge or accelerated inference optimization background, 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 ability to lead through technical influence.

Preferred qualifications

• Experience with quantization-aware training, post-training quantization, compression, pruning, or sparsity optimization.
• Experience deploying vision or multimodal models from development through production.
• Familiarity with ONNX and model graph-transformation pipelines.
• Experience validating numerical behaviour 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, or startup/founder experience building end-to-end AI platform capabilities.

Application information

• Third-party application services are not accepting applications for this role.