Senior AI Engineer

The Role

You will own the AI platform.

Freewyld Foundry runs a revenue management service for short-term rental operators. A fractional AI lead has built the foundation, the workflow orchestration, the LLM pipelines, the data plumbing, and it works. The next phase is bigger than a fractional role can carry: scaling from roughly 4,000 listings to an order of magnitude beyond, with all the reliability, cost discipline, and security that our work demands.

We are hiring our first full-time engineer to take full ownership of that platform. You will build the parts that do not exist yet, harden the parts that do, and keep the whole thing running in production. You will set the architecture and standards the engineering function grows on, working alongside our AI lead, who stays on for technical direction.

This is a hands-on building role, not an oversight one. You write the code, make the architecture calls, and are accountable for what happens after it ships.

About Freewyld Foundry

Freewyld Foundry is a high-performance revenue management firm serving short-term rental and hospitality operators.

We manage $190M+ in annual bookings across ~4,000 listings and deliver an average 18% RevPAR lift for clients. We are operators ourselves, and we build on real portfolios with real results. We are a small, fast-moving team that turns ideas into shipped product without heavy process, and we want an engineer who is energized by that pace, not slowed by the absence of a thick playbook.

What You’ll Own

Platform & Infrastructure

• Own async workflow orchestration (Temporal) end to end: extend what exists, design what does not, and keep it reliable under load
• Build observability, instrumentation, and alerting as first-class parts of the system so problems surface before clients feel them
• Own error handling, retries, idempotency, and queue management
• Set the architecture, patterns, and standards the rest of the engineering function will build on

AI Systems

• Own the LLM pipelines across providers (Anthropic, OpenAI), including prompt and tool design
• Build evaluation systems that measure AI output quality against ground truth. Shipping a good AI feature is half the job; knowing whether it is actually good is the other half
• Own model selection and cost management across providers as usage scales

Security & Data Hygiene

• Build prompt injection and abuse defenses into the platform
• Own secrets, credentials, and access control
• Treat guest, owner, and property data with the hygiene and care it requires
• Own vendor API key management (PriceLabs and others)

A-Player Defined

The engineer who thrives here has a real software engineering foundation and an equally real obsession with AI:

• Thinks in first principles of clean architecture, data hygiene, security, and long-term maintainability, and builds so the next engineer can move fast without breaking things
• Has shipped and operated production systems, owning cost, latency, and reliability, not just prototypes
• Validates AI outputs against ground truth, not impressions
• Instruments their own work, sets up alerts, and triages with discipline
• Ships a v1 and iterates rather than waiting for a perfect spec
• Is genuinely obsessed with leveraging AI and stays ahead of new tooling because they want to, not because they were told to

This is not a role for someone who wants a low-accountability remote job, or who needs to be managed rather than led. It is for an engineer who wants to own a real production system and the team standards around it.

What Success Looks Like

• The platform scales past today’s listing count without reliability or cost surprises
• AI output quality is measured against ground truth, and the evals prove it
• Cost, latency, and reliability are instrumented and trending the right way
• The architecture and standards you set let the team ship fast and safely

Required Experience

Must Have

• A traditional computer science or computer engineering foundation, with strong software development fundamentals (clean architecture, data hygiene, security, maintainability)
• 5+ years of backend or full-stack production experience
• At least one AI product shipped to real users where you owned cost, latency, reliability, and post-launch maintenance (notebooks and demos do not count)
• Strong eval discipline and end-to-end ownership instincts
• Fluency with modern AI coding assistants (Claude Code or peers), with judgment about when to trust their output
• A prototype-first working style: ship v1, then iterate
• Remote-first, async-fluent, with strong written English

Nice to Have

• Experience with Temporal or similar workflow orchestration
• Postgres and SQLite, REST and async APIs
• Familiarity with observability tooling (Sentry or similar)
• Next.js, React, Tailwind for frontend touch points
• Short-term rental, hospitality, or revenue-management domain experience
• A portfolio of shipped work: repos, deployed AI products, or eval suites you can walk us through

How We Work

AI fluency is core to how we operate, not a line item. We care less about which tools you used last quarter and more about whether you can keep evolving as fast as the tooling does. Tell us what you are building with today and what you have changed your mind about recently.

Interview Process

Final-stage candidates complete a paid test task before any offer: a small, scoped production problem that mirrors the actual work. It is how we both find out whether the fit is real. This step is non-negotiable.