Software Engineer, AI – San Francisco

SupportFinity™; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Health, dental, and vision coverage

Flexible time off

Job summary

A tech company specializing in AI solutions is seeking an AI/ML-focused Software Engineer to build innovative AI-powered features for requirements management. The ideal candidate will have over 3 years of experience in applied ML and strong software engineering skills. This role involves designing agentic workflows and integrating language models in a fast-paced environment, offering competitive salary, equity, and benefits including health coverage and flexible time off.

Qualifications

• 3+ years of experience in applied ML, LLMs, or adjacent fields.
• Strong software engineering fundamentals and service operation experience.
• Hands-on experience with modern LLM providers.

Responsibilities

• Design and ship AI-powered features for requirement management.
• Build agentic workflows for systems design and simulation.
• Evaluate and integrate language models and optimize them for production.

Skills

Applied ML

Software engineering fundamentals

Modern LLM providers and tooling

Prompt design

Evaluation methods

Comfortable in fast-paced environments

Tools

TypeScript/Node.js

Python

Postgres

Job description

About Flow

Flow Engineering is building an AI-native requirements platform for modern engineering organizations. Flow’s vision is to support “agentic systems engineers” and “agentic domain engineers” working alongside AI agents to design and operate complex systems.

About the role

Flow is seeking an AI/ML-focused Software Engineer 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

• 3+ years of experience in applied ML, LLMs, or adjacent fields, with a track record of shipping ML/LLM-powered features in production.

• 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.