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 We are looking for an Engineer / Research Scientist with deep expertise in quantized deep learning models for hardware acceleration in autonomous systems. In this cross-disciplinary role, you will bridge perception model design and hardware-aware deployment, enabling efficient execution of high-performance perception algorithms across embedded compute platforms. You will focus on researching state of the art perception models and develop optimization pipelines for the quantized versions of these models customized to provide real-time performance and energy efficiency on next-generation autonomy hardware. Responsibilities Research state of the art perception models in collaboration with the ADAS SW teams Lead the development of optimizations for mapping quantized perception models (e.g., CNNs, Transformers, LLMs) to embedded and heterogeneous hardware platforms. Design and implement hardware-aware optimizations, including quantization strategies, model compression, memory-efficient representations, and operator fusion, targeted to custom accelerators. Collaborate with hardware teams to co-optimize model architecture and compute pipeline under real-time constraints (latency, throughput, power). Benchmark and analyze system performance across platforms and iterate to achieve optimal deployment efficiency. Partner with perception, systems, and autonomy teams to align model optimization efforts with hardware roadmap and real-world autonomy requirements. Qualifications Ph.D. or M.S. in Computer Engineering, Electrical Engineering, Computer Science, or related field with a focus on ML compilers, embedded systems, or hardware-aware AI. Hands-on experience with quantized model deployment, ML design stacks, and code generation for embedded or heterogeneous compute systems. Strong understanding of computer vision models (e.g., object detection, segmentation) and their optimization for edge inference. Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and their low-level IRs or export formats (e.g., ONNX). Solid programming skills in C++, Python Familiarity with CUDA/OpenCL (or other accelerator programming models). Preferred Qualifications: Prior experience working with hardware-software co-design, especially for autonomous or robotics platforms. Deep knowledge of numerical precision trade-offs, quantization-aware training (QAT), and dynamic/static quantization flows. Familiarity with embedded real-time constraints and hardware profiling/debugging tools. Familiarity with rearchitecting models to best suit hardware capabilities. Publication record in top-tier ML/Systems conferences (e.g., MLSys, NeurIPS, DAC, ICCAD). Pay Disclosure Salary Range: The salary range for this role is $206,000 - $258,000. for San Francisco Bay Area based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs. The successful candidate may be eligible for annual performance bonus and equity awards. Benefits Summary: Rivian offers a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com. You can apply for this role through careers.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). This job is not expected to be closed any sooner than 3/30/2026. 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 candidateaccommodations@rivian.com. Candidate Data Privacy 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. Please note that we are currently not accepting applications from third party application services.
Staff ML Engineer, Hardware Software Co-design
This role focuses on optimizing quantized deep learning models for hardware acceleration in autonomous systems, bridging perception model design with efficient execution on embedded platforms. The engineer will research state-of-the-art perception models and develop optimization pipelines for quantized versions, customizing them for real-time performance and energy efficiency on next-generation autonomy hardware.
What you'd actually do
- Research state of the art perception models in collaboration with the ADAS SW teams
- Lead the development of optimizations for mapping quantized perception models (e.g., CNNs, Transformers, LLMs) to embedded and heterogeneous hardware platforms.
- Design and implement hardware-aware optimizations, including quantization strategies, model compression, memory-efficient representations, and operator fusion, targeted to custom accelerators.
- Collaborate with hardware teams to co-optimize model architecture and compute pipeline under real-time constraints (latency, throughput, power).
- Benchmark and analyze system performance across platforms and iterate to achieve optimal deployment efficiency.
Skills
Required
- Ph.D. or M.S. in Computer Engineering, Electrical Engineering, Computer Science, or related field with a focus on ML compilers, embedded systems, or hardware-aware AI.
- Hands-on experience with quantized model deployment, ML design stacks, and code generation for embedded or heterogeneous compute systems.
- Strong understanding of computer vision models (e.g., object detection, segmentation) and their optimization for edge inference.
- Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and their low-level IRs or export formats (e.g., ONNX).
- Solid programming skills in C++, Python
- Familiarity with CUDA/OpenCL (or other accelerator programming models).
Nice to have
- Prior experience working with hardware-software co-design, especially for autonomous or robotics platforms.
- Deep knowledge of numerical precision trade-offs, quantization-aware training (QAT), and dynamic/static quantization flows.
- Familiarity with embedded real-time constraints and hardware profiling/debugging tools.
- Familiarity with rearchitecting models to best suit hardware capabilities.
- Publication record in top-tier ML/Systems conferences (e.g., MLSys, NeurIPS, DAC, ICCAD).
What the JD emphasized
- quantized deep learning models
- hardware acceleration
- embedded compute platforms
- real-time performance
- energy efficiency
- quantized perception models
- custom accelerators
- real-time constraints
- optimal deployment efficiency
Other signals
- quantized deep learning models
- hardware acceleration
- embedded compute platforms
- real-time performance
- energy efficiency