Lead Data Scientist (Agentic Solutions)

Segment (Twilio); El Segundo, Irvine (CA, CA); On-site; USD 171,100 - 213,900; Full time

Benefits offered by this job

401(k) Plan

Employee Stock Purchase Plan

Paid time off

Job summary

Rivian seeks a Staff/Lead Data Scientist focused on Agentic Solutions to design and operationalize cognitive architecture powering Rivian's AI agents. You will build reasoning loops, retrieval systems, and evaluation frameworks enabling agents to act on live business data with accuracy and reliability.

The role emphasizes orchestration frameworks, SLM adaptation, and token‑economy optimization, with a focus on reliable, scalable AI for enterprise use in a fast-growing environment.

Qualifications

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Master’s or PhD preferred.

Responsibilities

• Design and program multi-step reasoning frameworks using LangChain, LlamaIndex, or Haystack.
• Architect advanced retrieval structures, experiment with embedding models, dynamic chunking strategies, and token management to ensure agents receive high‑fidelity business context.
• Proactive System Reasoning: Build internal logic and evaluation criteria for “Watchdog” agents, enabling them to analyze live operational data streams, infer anomalies, and generate proactive insights.
• Evaluate, select, and adapt small language models (e.g., Phi, Mistral, Gemma) for domain-specific tasks where precision, latency, and cost efficiency matter.
• Architect token-efficient design: context compression, prompt caching, dynamic context windowing, and minimizing calls for cost-effective scalability.
• Establish evaluation benchmarks to validate agent reasoning, track accuracy drift, and minimize hallucinations.

Education

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field

Master’s or PhD preferred

Tools

Databricks

Spark

Delta Lake

Job description

About Rivian

Rivian is on a mission to keep the world adventurous forever. We build emissions‑free electric adventure vehicles for curious, courageous souls who love the outdoors and want to protect it for future generations.

Role Summary

Rivian's AI team sits within the Analytics function of the Customer organization and builds the next generation of intelligent, autonomous systems that connect marketing, sales, and market intelligence. As a Staff/Lead Data Scientist focused on Agentic Solutions, you will design and operationalize the cognitive architecture that powers Rivian's AI agents—building reasoning loops, retrieval systems, and evaluation frameworks that allow agents to act on live business data with accuracy and reliability.

Responsibilities

Agent Orchestration & Cognitive Architecture

• Agentic Reasoning Loops: Design and program multi‑step reasoning frameworks using orchestration frameworks such as LangChain, LlamaIndex, or Haystack.
• Context Engineering & Advanced RAG: Architect advanced retrieval structures, experiment with embedding models, dynamic chunking strategies, and token management to ensure agents receive high‑fidelity business context.
• Proactive System Reasoning: Build internal logic and evaluation criteria for “Watchdog” agents, enabling them to analyze live operational data streams, infer anomalies, and generate proactive insights.

Applied Data Science & Model Optimization

• Small Language Model (SLM) Strategy: Evaluate, select, and adapt small language models (e.g., Phi, Mistral, Gemma) for domain‑specific agentic tasks where precision, latency, and cost efficiency outweigh raw model scale. Define the decision framework for when to use an SLM versus a frontier model based on task complexity, context requirements, and inference cost.
• Token Economics & Efficient Agent Design: Architect agents and orchestration frameworks around token efficiency as a first‑class design constraint—develop strategies for context compression, prompt caching, dynamic context windowing, and call minimization to ensure every inference call is purposeful and cost‑effective at scale.
• Domain‑Specific Model Adaptation: Execute parameter‑efficient fine‑tuning (e.g., LoRA/QLoRA) and model distillation on open‑source models to embed Rivian’s fulfillment jargon, vehicle logistics vocabulary, and internal business rules, prioritizing targeted SLM adaptation over large‑scale LLM retraining when feasible.
• Rigorous Evaluation Frameworks: Establish statistical, model‑driven, and human‑in‑the‑loop testing benchmarks to validate agent reasoning, track accuracy drift, and minimize hallucinations.
• Behavioral Prompt Engineering: Continuously iterate on complex system prompting, structured output formats (e.g., enforcing strict JSON/tool schemas), and cognitive guardrails to keep agent behavior deterministic and aligned.

Qualifications

Education & Experience

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Master’s or PhD preferred.
• 5+ years of experience in Machine Learning, Applied Data Science, or AI Engineering with a proven record of designing cognitive frameworks, RAG systems, or agentic workflows for business applications.

Skills & Expertise

• Expertise with modern AI orchestration frameworks (LangChain, LlamaIndex, Haystack) and deep understanding of LLM APIs, prompt engineering, and agent memory state management.
• Hands‑on experience with small language models (e.g., Phi, Mistral, Gemma) for domain‑specific deployment and the ability to reason about trade‑offs between model size, latency, cost, and task accuracy.
• Experience with parameter‑efficient fine‑tuning (LoRA/QLoRA) and model distillation.
• Deep understanding of token economics and experience designing agent frameworks that incorporate context compression, prompt caching, dynamic windowing, and call‑minimization strategies.
• Expert‑level proficiency with Python and SQL; comfortable using Databricks (Spark, Delta Lake) and working with structured schemas, metadata, and vector indices.
• Strong statistical foundation with familiarity in analyzing user behavioral data or event streams (e.g., Snowplow) to help agents deduce funnel health and operational anomalies.

Environment & Physical Requirements

• Proven ability to operate in a fast‑paced, high‑ambiguity environment such as a new product launch or startup‑stage AI build, with intellectual curiosity, rigorous attention to detail, and a bias for shipping.

Salary Disclosure

Salary range for this role is $171,100 to $213,900 for Irvine, CA and ElSegundo, CA based applicants. The range reflects the lowest to highest compensation the company is willing to offer at the time of posting. Compensation will be based on factors such as competencies, education, experience, location, shift, and organizational needs.

Benefits

We offer a comprehensive benefits package for full‑time and part‑time employees, their spouse or domestic partner, and children up to age 26. The package includes paid vacation, paid sick leave, and a competitive portfolio of insurance benefits—including life, medical, dental, vision, short‑term disability, and long‑term disability insurance. Eligible employees may also participate in Rivian’s 401(k) Plan and Employee Stock Purchase Plan. Full‑time coverage is effective on the first day of employment; part‑time coverage is effective on the first of the month following 90 days.

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 or domestic partner status, age, military or 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 that requires accommodations, please email [e-mail].