Staff AI/ML Engineer, Software Vehicle

About Rivian

Please read the following job description thoroughly to ensure you are the right fit for this role before applying.

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

At Rivian, our mission revolves around developing vehicles powered by software and data. As a Staff Machine Learning Engineer, you will be a crucial part of our team, working alongside engineers, data scientists, and domain experts to build models at scale, predict vehicle performance, and optimize SW parameters throughout the vehicle's lifetime.

We leverage the power of big data collected from our extensive customer and commercial fleets to achieve this.

Your responsibilities will include the development of traditional ML and Generative AI models and strategically leveraging the data from vehicle telemetry.

As a Staff Machine Learning Engineer at Rivian, you will contribute significantly to our commitment to pushing the frontiers of technological innovation in the automotive sector.

Responsibilities

• Work with LLM and AI / ML systems to drive impact through automation.
• Design and develop production backend distributed systems and microservices at scale.
• Be thoughtful about the customer experience and limitations of modern AI systems.
• Design, implement, and optimize deep learning models, training, and evaluation tools and infrastructure.
• Work cross-functionally with domain experts and other data stakeholders to specify, implement, and deliver meaningful insights for electric vehicle controls.
• Build deep learning models to enhance vehicle calibrations throughout its lifetime.

Qualifications

• M.S. or Ph.D. in Machine Learning, Engineering, Computer Science, or related STEM disciplines.
• Hands-on experience in training, fine tuning, and scaling up traditional ML models and LLMs, multimodal LLMs, efficient models, RL, RLHF, or RLAIF.
• Expert-level proficiency in Python and Apache Spark.
• In-depth understanding of statistical and machine learning concepts, including deep and reinforcement learning.
• Understanding of electric vehicle fundamentals is a plus.
• Excellent communication and interpersonal skills for both technical and non-technical audiences.
• Outstanding team player, capable of building consensus in a dynamic environment.

Pay Disclosure

Salary Range / Hourly Rate for California Based Applicants : $163K- 250K / year (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 premiums.

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].

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