Rivian
From Rivian's careers page
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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 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.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). This job is not expected to be closed any sooner than 12/1/26 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. 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. 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.
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