Software Engineer

⁠⁠You'll be a Full Stack Engineer who uses AI to build — and builds with AI. Two things at once: you ship real applications faster by treating LLMs as a team of junior developers you manage, and you build the AI use cases themselves — agents, RAG pipelines, LLM-powered products that work at scale. You'll work across the stack — cloud, backend, AI agents, frontend — and own what you build. We're building the Universal Electric Vehicle at Ford, doing things differently.

Orchestrate agents to build. Use AI as your default way of working — agents do the heavy lifting, you direct, review, and harden. You set the bar for how the team builds with AI.

Build the AI use cases. Design and ship AI-powered applications and agents — RAG pipelines, LLM integrations, agentic workflows. Understand what's happening under the hood and make it work in production, at scale.

Ship cloud-native systems on GCP. Design, deploy, operate. You own the architecture, the infrastructure-as-code, and the CI/CD pipeline. No throwing code over the wall.

Connect systems that don't want to be connected. Build the integration layer across enterprise SaaS platforms. Expect messy APIs, legacy constraints, and creative problem-solving.

Automate what shouldn't be manual. If a human repeats it, you write the code — or point an agent at it — to stop it.

Make other engineers faster. Build the tools, agent workflows, and guardrails that remove friction. Developer productivity is a multiplier.

BS in Computer Science, Electrical Engineering, or a related field. MS is a plus, not a substitute for shipping software.

2+ years building and deploying full-stack applications in production.

2+ years on cloud-native platforms (GCP preferred).

Demonstrable experience building AI agents or applications — show us what you've built, not what you've read.

Python — deep. You write production systems, not just scripts.

JavaScript / TypeScript — React, Node.js, or equivalent. A real frontend and a real API. Front-end strength is a big plus.

AI engineering — LLMs, embeddings, vector stores, RAG. Comfortable building agents and evaluation pipelines. Bonus for fine-tuning or eval work.

AI-assisted development — fluent with modern AI coding tools and agent workflows, and clear-eyed about where they help and where they don't.

GCP — Cloud Run, Cloud Functions, GKE, BigQuery, Cloud Storage. Deployed and operated, not just tutorials.

Databases — relational and NoSQL. You know when to use what, and why.

APIs & microservices — designed and built RESTful services at scale.

Git, IaC & CI/CD — Terraform, Cloud Build, or equivalent. Reproducible, version-controlled infrastructure and disciplined code review.