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Flow Engineering is an AI-native requirements platform for modern engineering organizations, enabling hardware teams to collaborate with AI agents to design, validate, and evolve complex systems with speed and rigor.
You will build 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 all the way to stable, observable features in production.
Key responsibilities:
- 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 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 quickly based on usage.
The company values speed over everything, prototyping AI workflows quickly then hardening what works. Ownership is clear per feature, from prototype to production. Fundamentals like evaluation, observability, and safety are built in from the first version.
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.
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.
