Sr. AI Engineer, Product Development

Rivian is hiring a Sr. AI Engineer for its Product Development AI and Data Science team in Tustin, CA (onsite). The role focuses on building agentic AI capabilities and AI-ready data foundations, then applying evaluation frameworks to automate engineering workflows for software-defined hardware.

What You’ll Do

• Partner with senior technical staff to design and orchestrate agentic AI workflows and LLM-powered systems that automate complex engineering activities, including documentation auditing, requirement generation, and technical knowledge retrieval.
• Build, validate, and maintain ETL pipelines and supporting data structures, including knowledge graphs and vector databases, to deliver high-fidelity context to AI applications across siloed engineering systems.
• Run rapid technical trials to assess emerging AI approaches, moving efforts from early concepts to functional prototypes to determine which methods best reduce engineering labor.
• Convert early prototypes into robust, highly performant, scalable enterprise systems.
• Define and monitor quantitative performance requirements, including accuracy, grounding, latency, and cost, aligned with safety and reliability expectations for vehicle engineering.
• Collaborate across teams to identify manual engineering workflows and implement AI-driven automations that improve product development efficiency and productivity.

Required Qualifications

• Bachelor’s, master’s, or PhD in a quantitative field (e.g., Computer Science, Electrical Engineering, Mechanical Engineering, Materials Science, Physics, Mathematics, or a related quantitative discipline).
• Ideally 4 to 6+ years of experience building production data pipelines and developing AI/ML solutions, with LLM-based application experience preferred.
• Experience in repository context engineering, AI-native IDEs and terminals, agentic loop optimization, and AI coding quality with technical debt mitigation.
• Hands-on or deep academic knowledge of LLM orchestration and application concepts, including RAG (Retrieval-Augmented Generation), agentic frameworks, context engineering, grounding, evaluation, and cost and latency optimization.
• Deep understanding of failure modes in AI systems versus traditional software, including statistical validation test design, tolerance thresholds, and tracking output distributions.
• Demonstrated experience with Git, eval-driven CI/CD pipelines, probabilistic validation of AI systems, deployment, and end-to-end system ownership for production environments.
• Experience with high-throughput storage systems, containerized harness orchestration, and security or sandbox environments.
• Ability to extract, clean, and structure data from technical documents, requirements, or engineering specifications.
• Prior experience in physical engineering systems (Hardware, IoT, or Telemetry).
• Strong problem-solving skills with an emphasis on product development.
• Excellent written and verbal communication skills for coordinating across teams and leading cross-functional efforts.
• A drive to learn and master new technologies and techniques.

Technologies

• LLM, RAG (Retrieval-Augmented Generation)
• ETL
• Knowledge graphs, vector databases
• Git, CI/CD
• Containerized harness orchestration
• Neo4j, Pinecone
• Spark, Databricks
• GCP, DBT

Location and Experience

Location: Tustin, CA (onsite)

Minimum experience: 4 years

Nice to Have

• Exposure to graph technologies (such as Neo4j or knowledge graphs) or vector databases (such as Pinecone).
• Knowledge of advanced statistical techniques (for example, regression, distribution properties, and appropriate use of statistical tests) and experience applying them.
• Knowledge of a range of machine learning techniques (such as clustering, tree-based methods, and deep learning) and understanding tradeoffs in real-world settings.
• Experience building user-facing applications.
• Experience with distributed data or computing tools, such as Spark, Databricks, and GCP.
• Experience with DBT.
• Interest in electric vehicles, renewable energy, and sustainable transportation.