Lead Data Scientist (Context Engineering)

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 Rivian’s AI team sits within the Analytics function of the Customer organization — the team that spans Marketing, Sales, and Market Intelligence — and is building the next generation of intelligent, autonomous systems that connect these functions into a single, reasoning ecosystem. As a Staff/Lead Data Scientist focused on Context Engineering, you will own the quality, safety, and economics of what AI agents retrieve and generate — building the evaluation frameworks, observability tooling, security and isolation controls, and memory systems that determine whether an agent’s answer is accurate, trustworthy, and worth what it costs to produce. This is a deeply technical, high-impact role at the frontier of applied AI, working closely with Agentic Solutions, Data Platform, and the broader Customer org Analytics team to bring agentic intelligence to production. Responsibilities Retrieval Quality & Observability Retrieval Quality & Evaluation: Build and own the evaluation framework for retrieval and generation quality — benchmarks, golden datasets, human-in-the-loop review, and production feedback signal — and use it to continuously audit the retrieval pipeline, closing the gap between what’s retrieved and what the agent needs. Context Observability & Production Ownership: Instrument the retrieval-to-answer path end to end so a bad answer can be root-caused to a specific retrieval, chunk, or prompt decision, and carry operational responsibility — triage, rollback, root-cause follow-up — when quality degrades in production. Data Freshness & Re-Indexing: Design the re-indexing and cache-invalidation strategy so fulfillment and sales data reflect real-time state rather than a stale snapshot — a distinct failure mode from relevance. Context Economics, Security & Memory Context Security & Isolation: Defend the generation layer against prompt injection and context leakage — isolating untrusted content, sandboxing tool outputs, and enforcing that an agent never surfaces data the end user isn’t authorized to see. Token Economics & Context Budget: Treat every token as a cost — design context compression, prompt caching, and dynamic context windowing to keep latency and inference cost down without degrading answer quality. Episodic & Context Memory: Design how agents remember across sessions — what’s retained, summarized, or forgotten — so prior context informs reasoning without overwhelming the context window. Qualifications Education: Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field; Master’s preferred. Experience: 8+ years in Applied ML, NLP, or ML Engineering operating production RAG or LLM-based retrieval systems. Evaluation & Benchmarking: Experience designing LLM evaluation frameworks — automated and human-in-the-loop — to measure accuracy, groundedness, and hallucination rate, and track drift over time. Production Operations & Observability: Experience instrumenting and tracing LLM/RAG pipelines (e.g., LangSmith, Arize, Phoenix) to debug production issues, carrying on-call ownership of quality or security incidents, and feeding production signal back into evaluation and freshness strategy. Context Security: Experience defending LLM systems against prompt injection and data leakage, including isolation, sandboxing, and authorization enforcement at the retrieval layer. Technical Mastery: Strong command of vector databases and retrieval libraries (e.g., LangChain, LlamaIndex, FAISS) and deep fluency in end-to-end retrieval pipelines. Expert-level Python, strong SQL. Token Economics: Experience optimizing LLM systems for cost and latency — context compression, prompt caching, dynamic context windowing — at production scale. Memory Systems: Experience designing episodic or long-term memory for conversational agents — what persists, how it’s compressed, how it’s retrieved. Environment: Comfortable in a fast-paced, high-ambiguity environment with a builder’s mentality and the ability to make principled decisions with incomplete information. Pay Disclosure The salary range for this role is $154,000 to $192,500 for Irvine, CA and El Segundo, CA 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. We offer 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 Plan 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.ri