Rivian
From Rivian's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
As a company, we constantly challenge what’s possible, never simply accepting what has always been done.
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 You will be a key member of the Perception team at Rivian, which develops advanced machine learning algorithms that directly impact safety critical self-driving features of our category defining vehicles. Responsibilities As a member of the Autonomy team, you will guide the architecture, implementation, and deployment of foundation models that act as learned world models. These models will support not only perception tasks (e.g., object detection, scene understanding) but also downstream decision-making and closed-loop autonomy. .Key areas of responsibility include: Developing technical strategy and architecture for foundation models as unified world models Developing multi-modal, multi-task transformer-based systems that support closed-loop autonomy Building training and evaluation pipelines at scale across petabytes of real-world and simulated driving data Collaborating with cross-functional teams across perception, planning, simulation, and ML infrastructure Driving alignment between model capabilities and real-world deployment constraints (latency, robustness, validation) Publishing internal technical guidance and mentoring engineers across autonomy ML Qualifications B.S., M.S., or Ph.D. in Computer Science, Robotics, or a related field 7+ years of experience building and deploying large-scale ML systems Deep understanding of foundation models, self-supervised learning, and world models in robotics or simulation Strong software engineering background, with fluency in Python and C++ Experience training and evaluating transformer models or end-to-end autonomous agents Familiarity with real-time inference systems and autonomous vehicle constraints Proven leadership in driving ML projects from research to production Bonus: Prior work on end-to-end autonomous driving architectures (e.g., imitation learning, behavior cloning, world models) Experience with sensor fusion (LiDAR, camera, radar) in a learned model 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 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. Interview Integrity & AI Policy Our interview process is designed to evaluate your personal technical foundation, real-time problem-solving logic, and structured capabilities. Therefore, the use of assistive generative AI tools to formulate or deliver your solutions during live interview sessions is not permitted. We care most about how you think — how you make sense of unclear requirements, weigh trade-offs, and explain why you chose one approach over another. We value your unique human perspective, your authenticity, and the journey it takes to reach a solution. Please note that we are currently not accepting applications from third party application services. As a member of the Autonomy team, you will guide the architecture, implementation, and deployment of foundation models that act as learned world models. These models will support not only perception tasks (e.g., object detection, scene understanding) but also downstream decision-making and closed-loop autonomy. .Key areas of responsibility include: Developing technical strategy and architecture for foundation models as unified world models Developing multi-modal, multi-task transformer-based systems that support closed-loop autonomy Building training and evaluation pipelines at scale across petabytes of real-world and simulated driving data Collaborating with cross-functional teams across perception, planning, simulation, and ML infrastructure Driving alignment between model capabilities and real-world deployment constraints (latency, robustness, validation) Publishing internal technical guidance and mentoring engineers across autonomy ML B.S., M.S., or Ph.D. in Computer Science, Robotics, or a related field 7+ years of experience building and deploying large-scale ML systems Deep understanding of foundation models, self-supervised learning, and world models in robotics or simulation Strong software engineering background, with fluency in Python and C++ Experience training and evaluating transformer models or end-to-end autonomous agents Familiarity with real-time inference systems and autonomous vehicle constraints Proven leadership in driving ML projects from research to production Bonus: Prior work on end-to-end autonomous driving architectures (e.g., imitation learning, behavior cloning, world models) Experience with sensor fusion (LiDAR, camera, radar) in a learned model
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