Senior Staff Software Engineer, AI Systems

About the role

Hybrid role (3 days onsite) in either San Francisco or New York

Comp: $200-300k, competitive equity and strong benefits

Sponsorship available and open to visa transfers (e.g. OPT, H1B transfers)

Interviews: 3 rounds, all virtual and beginning with a recruiter call

My client is a leading Legal AI company based in San Francisco and New York.

I am looking for senior or staff-level software engineers to build AI systems that power complex professional workflows: drafting documents, reviewing evidence, and moving cases toward completion.

You'll own the agents and infrastructure behind these workflows — orchestration, backend services, and evaluations that measure quality and catch regressions — working directly with domain experts to turn early experiments into reliable production systems.

What you'll do

Build agentic systems automating multi-step workflows, with human review at key points

Redesign task/workflow orchestration for long-running work, dependencies, retries, and recovery

Build evaluation systems to measure quality, catch regressions, and guide improvements

Improve backend services, event pipelines, and scheduled job infrastructure

Make agent execution observable for debugging, quality, latency, and cost management

Connect systems to internal tools and a user-facing portal, including supervision APIs/interfaces

You might be a good fit if you:

Have 4+ years shipping production software, with deep backend and AI/ML application experience in a fast-growing startup

Have built with LLMs beyond basic chat, with real thought on tool use, context, and handling unreliable outputs

Can translate domain experts' nuanced judgment into software behavior and evaluations

Fluent with Python and TypeScript

Have led projects end-to-end, from ambiguous requirements to deployment and operation

Understand workflow orchestration and async systems (queues, events, retries, idempotency)

Have high agency to research, prototype, and drive solutions independently

Experience with production agents, eval infrastructure, or document workflows is especially relevant. Engineering judgment matters more than familiarity with a specific framework.

Culture: Engineering works closely with ops, product, and customers; async-first with protected focus time; strong craft standards; small, tight-knit team.