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AI Solutions Engineer II

Job in Irvine, Orange County, California, 92713, USA
Listing for: Rivian
Full Time position
Listed on 2026-03-15
Job specializations:
  • Engineering
    AI Engineer, Data Science Manager, Data Engineer, Software Engineer
Job Description & How to Apply Below
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

We are looking for an AI Solutions Engineer who will support our Product Development organization by implementing scalable AI tools that improve Product Development efficiency and productivity. As an AI Solutions Engineer on the Product Development AI and Data Science team, you will be at the center of Rivian's AI flywheel. Our mission is to bring clarity, rigor, and scale to the complex decisions required to build the world's most adventurous electric vehicles.

We're building an intelligent operating system for engineering. You will spend your time at the intersection of product development, software engineering, data science, and LLM orchestration. You will work closely with senior technical staff to automate manual engineering workflows, build predictive models, and deploy agentic AI tools that allow our engineers to focus on innovation rather than administration.

The ideal candidate has an understanding of engineering processes for physical products and experience working with IoT, telemetry, and Product Lifecycle Management data. They are capable of developing technical plans in close collaboration with senior technical staff and a wide range of stakeholders and functional teams. They are adept at evaluating existing processes and data ecosystems to find opportunities for optimization and using quantitative approaches to test the effectiveness of different courses of action.

The right candidate will be able to assess ambiguous problem spaces and determine the right technical approach ranging from traditional analytics to data science and machine learning to LLM applications.

They must have strong experience using a variety of data analysis methods, using a variety of data tools, building and implementing models including AI/LLM applications, and a demonstrated ability to quickly ramp up on domain-specific data systems.

Responsibilities
  • Design & Deploy AI Agents:
    Partner with senior technical staff to build and orchestrate agentic AI workflows and LLM-powered systems (e.g., RAG, Graph

    RAG) that automate complex engineering tasks such as documentation auditing, requirement generation, and technical knowledge retrieval.
  • Construct AI-Ready Data Layers:
    Develop and maintain the specialized data structures- including knowledge graphs and vector databases-required to provide high-fidelity context to AI applications across siloed engineering systems.
  • Execute AI Trials:
    Lead rapid, high-velocity technical trials to evaluate the viability of emerging AI techniques. You will take projects from initial concept to functional prototype to identify which solutions can most effectively reduce engineering labor.
  • Bridge Prototype to Production:
    Build hands-on tools starting from early prototypes to reliable, production-ready solutions that are performant and scalable.
  • Develop Evaluation Frameworks:
    Define and monitor quantitative performance requirements (accuracy, grounding, latency, and cost) to ensure AI tools meet the rigorous safety and reliability standards of vehicle engineering.
  • Engineering Process Optimization:
    Collaborate with cross-functional partners to identify manual engineering workflows and implement AI-driven automations that improve overall product development efficiency and productivity.
Qualifications
  • Bachelor's, master's, or PhD in Computer Science, Electrical Engineering, Mechanical Engineering, Materials Science, Physics, Mathematics, or another quantitative field
  • 0-4 years of experience building production data pipelines and developing AI/ML solutions (LLM- based applications preferred).
  • High level of programming proficiency in Python and SQL. You should be comfortable…
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