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AI Solutions Engineer II
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.
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Design & Deploy AI Agents: Partner with senior technical staff to build and orchestrate agentic AI workflows and LLM-powered systems (e.g., RAG, GraphRAG) that automate complex engineering tasks such as documentation auditing, requirement generation, and technical knowledge retrieval.
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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.
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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.
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Bachelor’s, master's, or PhD in Computer Science, Electrical Engineering, Mechanical Engineering, Materials Science, Physics, Mathematics, or another quantitative field
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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 writing clean, modular code and querying complex relational databases.
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AI & LLM Engineering: Hands-on experience or deep academic exposure to LLM orchestration and application concepts, including RAG (Retrieval-Augmented Generation), agentic frameworks, context engineering, grounding, evaluation, and cost/latency optimization.
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Experience with Unstructured Data: Ability to extract, clean, and structure data from technical documents, requirements, or engineering specifications.
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Software Fundamentals: Demonstrated experience with version control (Git) and an understanding of how to move a model from a notebook to a stable, reproducible environment.
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Domain Context: Prior experience or interest in physical engineering systems (Hardware, IoT, or Telemetry).
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Strong problem-solving skills with an emphasis on product development
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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
NICE TO HAVE:
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Exposure to graph technologies (e.g., Neo4j, Knowledge Graphs) or vector databases (e.g., Pinecone).
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Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
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Knowledge of a variety of machine learning techniques (clustering, tree-based methods, deep learning, etc.) and their real-world advantages/drawbacks
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Experience building user-facing applications using Plotly Dash.
- Experience with distributed data/computing tools: Spark, Databricks, etc.
- Experience with DBT.
- Interest in electric vehicles, charging, and clean/renewable energy
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