Senior Applied AI Engineer

• This is a full-time, in-person role based in San Francisco (Presidio) - we work from the office 5 days a week.

• You must be based in the Bay Area or willing to relocate before starting.

• We require US work authorisation, but are open to O-1 or J-1 visa sponsorship for exceptional candidates.

Every tool you use was built as an island, so you became the glue between them: a name from Gmail into the CRM, a time from the CRM into Calendly, a note from Calendly into a doc. AI showed up and mostly became an eleventh window to paste into.

Sauna is a hybrid of AI and software standing on one shared brain. It holds your context and hands you real software built on top of it, so an ordinary Calendly becomes one that writes the invite itself and knows which of your busy hours bend, and for whom. Software for the path you repeat, AI for the edge that never repeats.

Wordware is the team behind it, 15 people backed by Spark Capital, Felicis, and Y Combinator with a $30M seed, the largest in YC's history. We build from an office in the Presidio, fifty metres from Crissy Field beach, and we work absurdly hard because we can see and feel the outcome every day.

As a Senior Applied AI Engineer, you’ll be responsible for building, refining, and scaling the agent systems inside Sauna, from architecture to evals to deployment. This is a senior seat. You own those systems, and when one breaks for a customer, that's on you.

We care about what works in production: fast response times, predictable behavior, traceability, and uptime.

A lot of what you'll decide has no playbook yet. We want someone who has already made calls like these on a real agent system and can tell us how they turned out.

You’ll work across infra, frontend, and product to make sure the agents people build inside Wordware actually work. You'll also have a say in how the rest of the team builds them.

• Implement multi-step, tool-using agents that hit real APIs and handle retries, auth, timeouts, and edge cases.

• Design agent memory systems that persist relevant state across runs, e.g. memory migrations, context organization, and orchestration state.

• Create agents that proactively do work and send you reminders.

• Own and evolve our eval framework: both automated checks and human-in-the-loop scoring.

• Dig into production failures from traces and fix the underlying system.

• Plus whatever else you see fit.

• Pick the agent problems worth solving without waiting for a ticket.

• Make architecture decisions with incomplete information and write down why you made them.

• Decide what counts as reliable for our agents, and hold releases to it.

• Review other people's code and design docs, and push the quality up.

Heads-up: this is a senior role. We're looking for someone who has run an agent system in production and made the architecture calls on it.

• 4+ years of engineering experience, including time shipping production software.

• You've built and deployed agent-like systems: multi-step LLM pipelines, tool-using bots, scripted assistants, or similar.

• You ran at least one of them in production, made the architecture calls on it, and can explain the tradeoffs.

• Hands-on experience with:

  • Agent orchestration and memory management (e.g. memory migrations, state organization).

  • Tool use and orchestration (e.g. calling real APIs, using plugins, auth flows)

  • Evaluation: success metrics, regression testing, and improving agent behavior over time

• You write production-grade code and can work across systems without needing a spec.

• You'd rather ship than polish forever.

• Shipped agents that live in the wild, used by customers, not just internal demos.

• Familiarity with LLM ops, tracing, observability, and failure handling.

• You've been a founder or early engineer, and it shows in the bar you hold your own work to.

• You've mentored or led other engineers.

Base salary: $180K–$240K + meaningful early-stage equity + health, dental, 401(k), considerable PTO, gym budget, lunch.

We keep our process simple. Exceptional candidates go from first touch to offer within 2 weeks.

1. Application: Submit your resume and answer a few quick questions.

2. 15-min intro call: Quick check to align on location, motivation, and logistics. If it’s a go, we move fast from here.

3. System design interview (1 hour): We dig into how you think about agent design: architecture, tradeoffs, and your experience building AI harnesses and agent systems.

4. Technical interview (optional follow-up): A coding round testing hands-on engineering fluency and speed, only if we need a closer look.

5. Final conversation: Answer any questions and scope out the work trial.

6. Work trial: Paid, in-person. Typically 5 days, though we're flexible on timing depending on the role. You’ll work on something meaningful with us.