Sr. Manager, Hardware, Machine Learning/AI

About Rivian Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.

Summary Rivian is looking for a Sr Manager of Machine Learning/AI Engineering to lead the teams expanding the capabilities of our ML application and compiler ecosystem for edge AI platforms. In this role, you will serve as a strategic technical leader responsible for our ability to port a broad set of models onto RAP1-class platforms, oversee the development of model enablement infrastructure, and help define the long-term hardware-software roadmap for the organization. The role requires a leader at the intersection of machine learning, model optimization, compiler/toolchain development, and hardware-aware deployment. You will manage a high-performing team building practical inference systems for vision-heavy models on constrained edge hardware, partnering closely with ML, compiler, and silicon leadership to drive technical depth and execution velocity across the company.

Direct the development and strategy of machine learning enablement workflows for Rivian edge AI platforms. Oversee the roadmap for front-end compiler and model-ingestion tooling to support an expanding model zoo on RAP1 and future platforms. Drive the architectural vision for robust quantization-aware training and model optimization infrastructure at scale. Manage the end-to-end process of porting, optimizing, and benchmarking machine learning models for hardware accelerators and NPUs. Lead cross-functional initiatives with hardware and systems teams to optimize performance across memory, scheduling, and data-movement constraints. Establish rigorous standards and methods for evaluating model fidelity and deployment quality across diverse execution paths. Collaborate with executive leadership to identify highest-value workloads and align technical execution with Rivian's product milestones. Recruit, mentor, and grow a world-class team of engineers specializing in model deployment, compiler infrastructure, and hardware-aware ML. Help shape the long-term direction of Rivian’s edge AI stack, including support for increasingly large and complex neural network models.

Qualifications Proven experience leading or managing teams in machine learning systems, model deployment, or AI infrastructure. Strong background in model optimization 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 working with low-power edge platforms, NPUs, custom accelerators, GPUs, or closely related architectures. Ability to reason about memory allocation, execution scheduling, bandwidth constraints, and overall system performance. Experience translating research concepts into practical tooling and production workflows. Exceptional cross-functional leadership skills and a history of driving impact through technical and strategic influence.

Experience with quantization-aware training, post-training quantization, compression, pruning, and sparsity optimization. Experience bringing vision or multimodal models from development through full deployment. Familiarity with ONNX and model graph transformation pipelines. Experience validating numerical behavior across float and integer execution paths. Experience supporting external customers, partner enablement, or productization of AI