Role: Senior AI Engineer

Hybrid: Menlo Park, CA

Compensation: $150,000 - $200,000 + Equity, Benefits

About RepairWise

RepairWise is an AI-powered repair platform built for the EV era. The US automotive repair market exceeds $300B annually — and EV repair complexity is growing faster than the industry’s tooling can keep up. We partner with 90+ active shops (up from 18 less than a year ago), have processed over $4M in repair GMV, and are growing rapidly. Our small, high-ownership engineering team moves fast and builds things that matter in a market that’s changing by the month.

Why Us?

The automotive repair industry is ripe for AI disruption but deeply underserved by good tooling. Our agents handle real-world complexity: ambiguous diagnostic codes, warranty policy interpretation, technician-level reasoning about vehicle systems. We’re now moving beyond API-driven AI to training domain-specific models on our own repair data — a genuinely hard problem with real consequences. If you want to own the full AI stack at a company where your work directly shapes the product, this is the role.

The Role

We’re looking for a Senior AI Engineer to own and lead AI development across our platform. This is a hands-on, high-ownership role — you’ll architect, build, and ship our AI systems end-to-end, not just implement what others design. You’ll work directly with our Head of Engineering, have a seat at the table on product direction, and be the person who sets the technical bar for AI at RepairWise.

Our AI work spans LLM orchestration, agentic workflows, and — increasingly — training domain-specific models on our proprietary repair data. We’re not doing academic research. We’re building AI that helps shop technicians diagnose vehicles, service advisors write repair orders, and operators process warranty claims at scale.

What You’ll Work Do

Core AI Agents

Architect, build, and evolve the AI agents that power RepairWise’s core workflows — from repair intake through resolution. You’ll own their design, reliability, and continuous improvement.

Model Training & Fine-Tuning

Train and fine-tune domain-specific models on our proprietary repair data for two high-value use cases: automated repair plan creation and warranty claim writing. This includes owning the full pipeline — dataset curation, annotation, training, evaluation, and deployment.

Diagnostic Pipelines

We ingest and process vehicle telemetry and diagnostic data from OEM sources. You’ll help us build smarter, faster, and more accurate diagnostic systems as we expand our vehicle coverage.

Data Infrastructure

Our analytics stack is built on DuckDB/MotherDuck. You’ll work with structured repair order data, labor codes, and shop performance metrics to train, evaluate, and improve model outputs.

Evaluation & Quality

Own evals, feedback loops, and guardrails for both LLM-powered agents and fine-tuned models — in a domain where accuracy has direct financial consequences for our shop partners.

Our Stack

LLM APIs (Anthropic, OpenAI) • Mastra / LangGraph • PyTorch • Hugging Face Transformers • DuckDB / MotherDuck • PostgreSQL • Python • TypeScript • AWS • Docker

What You’ll Bring

Must-haves:

5–8 years of software engineering experience, with at least 3 years in applied AI/ML or LLM systems
Proficiency in Python — our primary language for AI/ML work
Hands-on experience building and shipping LLM-powered products — RAG pipelines, agents, tool-calling, prompt engineering
Experience with LLM APIs (Anthropic, OpenAI, or similar) and modern orchestration patterns
Demonstrated experience fine-tuning or training models using PyTorch and Hugging Face Transformers
Experience owning dataset curation and annotation pipelines for model training
Experience building evaluation and observability tooling for LLM systems — evals, feedback loops, and guardrails
Ability to own systems end-to-end — from architecture decisions through deployment and monitoring
Comfort working with structured data and SQL — our domain is rich tabular data, not just text
Product instinct — you care whether the thing you built actually helps the user

Nice-to-haves:

TypeScript proficiency
Experience with agentic frameworks (Mastra, LangGraph, LlamaIndex, or similar)
Familiarity with PEFT/LoRA or other parameter-efficient fine-tuning techniques
Experience with experiment tracking tools (Weights & Biases, MLflow, or similar)
Familiarity with vector databases (Pinecone, Weaviate, pgvector, or similar)
Familiarity with DuckDB or columnar databases
Automotive, insurance, or field service industry experience
Background in multi-agent system design

What We Offer -->

Salary range of $150,000–$200,000 depending on experience plus equity
Meaningful equity with real upside — you’re joining early as one of the first engineers on the AI function
Comprehensive health, dental, and vision coverage
Preferred hHybrid schedule — 2 days/week in our Menlo Park office
Direct access to company leadership and real ownership over your work
A problem space — EV diagnostics and warranty processing — that is genuinely hard and genuinely interesting
We are not able to sponsor work visas at this time

Equal Opportunity

RepairWise is an equal opportunity employer. We welcome applicants of all backgrounds and encourage you to apply even if you don’t meet every qualification listed — we care more about what you’ve built and how you think than whether your resume checks every box.

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