The posting
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. Role Summary Rivian is looking for a Staff Machine Learning/AI Engineer to help expand the capabilities of our ML application and compiler ecosystem for edge AI platforms. This role is intended for a senior technical leader who can strengthen our ability to port a broad set of models onto RAP1-class platforms, improve model enablement infrastructure, and help shape the next phase of our hardware-software roadmap. The role sits at the intersection of machine learning, model optimization, compiler/toolchain development, and hardware-aware deployment. The ideal candidate brings deep experience building practical inference systems, understands the realities of deploying large and vision-heavy models on constrained edge hardware, and can partner closely with ML, compiler, and silicon teams to raise both technical depth and execution velocity. Responsibilities Lead development of machine learning enablement workflows for Rivian edge AI platforms. Build and improve front-end compiler and model-ingestion tooling needed to support a broad model zoo on RAP1 and future platforms. Develop and scale robust quantization-aware training and model optimization infrastructure. Port, optimize, validate, and benchmark machine learning models for deployment on hardware accelerators and NPU-class architectures. Improve performance across memory, scheduling, sparsity, and data-movement constraints in close collaboration with hardware and systems teams. Create methods to evaluate model fidelity and deployment quality across floating-point and quantized paths, including accuracy, error, and performance tradeoffs. Work across software, compiler, architecture, and product teams to identify the highest-value workloads and accelerate customer-relevant enablement. Provide technical leadership and mentoring for engineers working on 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 Qualifications Deep experience 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. Strong cross-functional communication skills and the ability to lead through technical influence. Preferred qualifications 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 platforms. Background working with both model-level and hardware-level optimization problems. Startup or founder-level experience building end-to-end AI platform capabilities. Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law. Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at [email protected]. Candidate Data Privacy and Technology Rivian may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. Rivian may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. Rivian may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions. How We Use AI in Our Hiring Process: To ensure transparency, we want candidates to know that Rivian uses iCIMS Talent Cloud Artificial Intelligence (TCAI) and AI-enabled tools to assist with screening, reviewing, organizing and highlighting profiles and applications that match the key requirements for each role. AI does not make hiring decisions: Qualified candidate applications are reviewed by a member of our team, and all decisions throughout the process are made by humans. We use AI to support efficiency and consistency, not to replace human judgment. We are committed to a fair, thoughtful, and equitable experience for every candidate. Participation in AI profile matching is entirely voluntary. If you prefer that your profile not be used in this process, you can opt out at any time. Opting out means your profile will be excluded from automated matching and will not be surfaced for additional roles through this system. Your current application remains active and will not be affected in any way. Please note that we are currently not accepting applications from third party application services.
Lead development of machine learning enablement workflows for Rivian edge AI platforms. Build and improve front-end compiler and model-ingestion tooling needed to support a broad model zoo on RAP1 and future platforms. Develop and scale robust quantization-aware training and model optimization infrastructure. Port, optimize, validate, and benchmark machine learning models for deployment on hardware accelerators and NPU-class architectures. Improve performance across memory, scheduling, sparsity, and data-movement constraints in close collaboration with hardware and systems teams. Create methods to evaluate model fidelity and deployment quality across floating-point and quantized paths, including accuracy, error, and performance tradeoffs. Work across software, compiler, architecture, and product teams to identify the highest-value workloads and accelerate customer-relevant enablement. Provide technical leadership and mentoring for engineers working on 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 Deep experience 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. Strong cross-functional communication skills and the ability to lead through technical influence. Preferred qualifications 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 platforms. Background working with both model-level and hardware-level optimization problems. Startup or founder-level experience building end-to-end AI platform capabilities.
