Software Engineer, Full Stack – San Francisco

The Engineering Company; San Francisco (CA); On-site; Full time

Benefits offered by this job

Competitive salary

Health, dental, and vision coverage

Flexible time off

Job summary

A technology firm is seeking a Full Stack Software Engineer to develop AI-driven features for managing requirements. You will design systems that integrate language models, optimize workflows, and create infrastructure for data management. Ideal candidates will have strong software engineering skills and hands-on experience with modern LLMs. The role offers a competitive salary and benefits including health coverage and flexible time off, in a fast-paced, innovative environment.

Qualifications

• Experience designing, testing, and operating services at scale.
• Hands-on with modern LLM providers like OpenAI and Hugging Face.
• Ability to evaluate safety and guardrails in AI systems.

Responsibilities

• Design and ship AI-powered features for requirement management.
• Build workflows for agentic systems and domain engineers.
• Integrate language models while optimizing for reliability.

Skills

Strong software engineering fundamentals

Hands-on experience with LLMs and tooling

Familiarity with prompt design

Ability to reason about tradeoffs

Comfortable in a fast-paced environment

Tools

TypeScript/Node.js

Python

Postgres

Job description

About Flow

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.

About the role

Flow is seeking Full Stack Software Engineers to build AI-powered capabilities that help teams author, review, and manage requirements more effectively. You will work on agentic systems engineer and agentic domain engineer workflows—bringing AI into the center of how teams reason about their systems.

This role sits at the intersection of AI, product, and full-stack engineering: you will take ideas from prototype all the way to stable, observable features in production.

What you’ll do

• 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 “agentic systems engineers” and “agentic domain engineers” 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 the surrounding infrastructure: data pipelines, evaluation harnesses, prompt and model management, observability, and safety/guardrails.

• Work across the stack—from backend integrations and APIs to simple UI hooks—to deliver complete AI features, not just model endpoints.

• Partner with product and customers to identify high-value workflows, run experiments, and iterate quickly based on usage.

About you

• Strong software engineering fundamentals, 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.

Our stack (AI‑leaning)

• TypeScript/Node.js and Python for AI and backend services.

• Modern LLM APIs and orchestration libraries for building agentic workflows.

• Postgres and other managed cloud services for data and state.

How we work & values

• Speed over everything: prototype AI workflows quickly, then harden what works.

• Own, downscope, ship, iterate: one clear owner per feature, from prototype to production.

• Fundamentals done well: evaluation, observability, and safety are part of the first version, not an afterthought.

• Competitive salary and meaningful equity.

• Health, dental, and vision coverage.

• Flexible time off and support for experimentation, learning, and staying current with the AI ecosystem.