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Flow Engineering is an AI-native requirements platform for modern engineering organizations. The company enables hardware teams to collaborate with AI agents to design, validate, and evolve complex systems with speed and rigor.
You will join as a Senior Product Engineer, working on full-stack development of AI-powered capabilities that help teams author, review, and manage requirements more effectively. This role sits at the intersection of AI, product, and full-stack engineering, taking ideas from prototype through to stable, observable features in production.
Key responsibilities include:
- Design and ship AI-powered features such as assisted requirement drafting, consistency checks, impact analysis, and intelligent suggestions for systems and domain engineers
- Build agentic workflows that help teams explore designs, simulate changes, and validate requirements
- Evaluate and integrate language models and related tooling, optimizing for reliability, latency, cost, and debuggability in production
- Build and maintain surrounding infrastructure: data pipelines, evaluation harnesses, prompt and model management, observability, and safety/guardrails
- Work across the full stack—from backend integrations and APIs to UI—to deliver complete AI features
- Partner with product and customers to identify high-value workflows, run experiments, and iterate based on usage
The company values speed over everything, with a philosophy of prototype quickly then harden what works. You'll own features end-to-end with clear ownership, and fundamentals like evaluation, observability, and safety are built in from the start, not as afterthoughts.
Tech stack: TypeScript/Node.js and Python for AI and backend services; modern LLM APIs and orchestration libraries for agentic workflows; Postgres and managed cloud services.
REQUIREMENTS:
- 5+ years developing production software, including designing, testing, and operating services at scale in a cloud environment
- Hands-on experience with modern LLM providers and tooling (e.g., OpenAI, Anthropic, Hugging Face, vector stores, RAG patterns)
- Familiarity with prompt design, retrieval-augmented systems, evaluation methods, and safety/guardrail approaches
- Ability to reason about tradeoffs between different models, architectures, and deployment patterns and make pragmatic decisions
- Comfortable working in a high-ownership, fast-paced environment where experiments and iteration are the norm
