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 Failsafe is a foundational pillar of the Autonomy safety architecture. In this Staff ML Software Engineer role, you will play a key part in driving and delivering high-quality, real-time onboard models and algorithms to evaluate sensor reliability, scene hazards, and perception capabilities. You will design, train, and ship production-grade solutions that enable our vehicles to understand environmental limitations, sensor degradations, and safety boundaries in 3D. As an experienced technical expert, you will own the end-to-end lifecycle of the failsafe system: defining safety metrics, engineering diagnostic algorithms, benchmarking on-vehicle performance, and establishing tight feedback loops. A key focus of this role is dedicated to multi-modal scene understanding, ensuring the vehicle can gracefully handle degraded states (such as lens blockage, adverse weather, or road surface hazards) to trigger automated sensor recoverability routines or minimum risk maneuvers. Responsibilities Lead the design, delivery and deployment of production-grade, highly reliable failsafe algorithms for L3 and L4 platforms, focusing on real-time sensor blockage, degradation, and occlusion detection. Develop multi-modal (camera and/or LiDAR) models to analyze real-time road conditions, including surface wetness, friction/mu-estimation, and physical drivability hazards. Design intelligent algorithms to trigger automated sensor-cleaning hardware and recovery strategies, directly maximizing ADAS uptime in challenging environments Architect systems to evaluate and report real-time 3D perception uncertainty and capability limits, interfacing directly with downstream planning and central safety fallback managers. Partner closely with cross-functional teams to define strict safety boundaries, establish rigorous testing benchmarks, and guide system-level architecture trade-offs. Qualifications BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field. 7+ years of professional experience scaling production-grade MLsoftware for autonomy or robotics. Proven track record of hands-on experience building algorithms for autonomous vehicle perception or environmental scene understanding. Solid understanding of modern perception pipelines and data representation across multiple modalities (cameras, LiDAR, and Radar). Strong proficiency in C++ and Python alongside experience with real-time software performance optimization and latency profiling. Demonstrated ability to drive complex technical roadmaps and deliver components in a fast-paced environment. Preferred Qualifications Experience designing failsafe, fail-operable, or degraded-state software architectures for safety-critical systems. Hands-on experience with sensor degradation modeling (such as camera soil, lens glare, rain, or LiDAR spray) and system-level ODD degradation modeling. Deep understanding of onboard edge deployment and managing cloud telemetry loops to monitor model performance in the field. Experience collaborating directly with systems safety teams to translate hazard analysis and risk assessments into robust software implementations. Pay Disclosure Salary Range for California Based Applicants: $228,000 - $285,000 (actual compensation will be determined based on experience, location, and other factors permitted by law). Benefits Summary: Rivian provides robust medical/Rx, dental and vision insurance packages for full-time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian covers most of the premium 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. Lead the design, delivery and deployment of production-grade, highly reliable failsafe algorithms for L3 and L4 platforms, focusing on real-time sensor blockage, degradation, and occlusion detection. Develop multi-modal (camera and/or LiDAR) models to analyze real-time road conditions, including surface wetness, friction/mu-estimation, and physical drivability hazards. Design intelligent algorithms to trigger automated sensor-cleaning hardware and recovery strategies, directly maximizing ADAS uptime in challenging environments Architect systems to evaluate and report real-time 3D perception uncertainty and capability limits, interfacing directly with downstream planning and central safety fallback managers. Partner closely with cross-functional teams to define strict safety boundaries, establish rigorous testing benchmarks, and guide system-level architecture trade-offs. BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field. 7+ years of professional experience scaling production-grade MLsoftware for autonomy or robotics. Proven track record of hands-on experience building algorithms for autonomous vehicle perception or environmental scene understanding. Solid understanding of modern perception pipelines and data representation across multiple modalities (cameras, LiDAR, and Radar). Strong proficiency in C++ and Python alongside experience with real-time software performance optimization and latency profiling. Demonstrated ability to drive complex technical roadmaps and deliver components in a fast-paced environment. Preferred Qualifications Experience designing failsafe, fail-operable, or degraded-state software architectures for safety-critical systems. Hands-on experience with sensor degradation modeling (such as camera soil, lens glare, rain, or LiDAR spray) and system-level ODD degradation modeling. Deep understanding of onboard edge deployment and managing cloud telemetry loops to monitor model performance in the field. Experience collaborating directly with systems safety teams to translate hazard analysis and risk assessments into robust software implementations.
