AI Engineer

About This Role

Twenty80 is partnering with a fast-moving technology company to find an AI Engineer to build and ship LLM-powered applications. Our client is looking for someone to own new AI workflows from prototype to production. Expect to ship fast, talk directly to customers, and build systems that solve real problems for users.

This is a full-time onsite position. You will work five days a week from our client’s office in San Francisco, collaborating in person with PMs, designers, and the GTM team to ship fast and stay close to the work.

This is a deeply technical role that blends ML, product thinking, and engineering. You will build and own production services that rely on LLMs and speech models.

What you’ll do

• Develop and own end-to-end AI applications across a range of workflows, from copilots to automation tooling and beyond.
• Improve our client’s LLM-powered platform, used daily by thousands of users.
• Build and optimize inference pipelines and real-time speech recognition systems.
• Collaborate closely with PMs, designers, and GTM teams to iterate rapidly and launch high-impact features.

Suggested Experience

• 3+ years building production AI systems, ideally in fast-moving environments or as a founder.
• Extensive experience with LLM inference (OpenAI, Anthropic, Llama, etc.).
• Strong product sense.
• Comfortable working across the stack (frontend, backend, etc.) to get features shipped.
• Obsessed with speed, ownership, and getting real user feedback.

This is a remote position, although Bay Area residents are encouraged to work from the client’s SF office.

Nice to Haves

• Experience with ASR systems (e.g. Whisper).

Seniority

2 to 6 years of experience as a software engineer, with AI and LLM-focused product work.

Work experience

• Must have built complex AI applications end-to-end (LLM inference work, agent products, etc.).
• Experience in a fast-moving startup or as a founder.

Education

CS undergraduate degree from a four-year university.

Hard skills

• Able to work across the stack (frontend, backend, infra) to problem solve and ship features.
• Applied-AI literacy. Can reason about evals, statistics, and the non-determinism of LLM systems.
• Familiarity with SOC-2 or sensitive data pipelines.

Soft skills

Obsessed with speed, ownership, and getting real user feedback.

Traits to avoid

Lack of progression in role after 2 to 3 years.