Forward-Deployed AI Engineer
Truss
Apply on the employer’s site →
Forward-Deployed AI Engineer Remote (US or Canada) · Full-time · Engineering · 6+ years Canada remote: CAD 150K – 250K · US remote: USD 125K – 175K · 0.25% – 0.50% equity WE ALSO OFFER IN PERSON IN AUSTIN TEXAS STARTING JAN 1 2026 Sanity Check Remote is this seat. US or Canada. TN is OK if you are Canadian. We are not sponsoring other visas. Pay is in the currency listed for that location: CAD if you are remote in Canada, USD if you are remote in the US. You have been a working engineer for at least six years. This field is new. We do not expect six years of "AI engineer" on a resume. We do expect you have shipped real software for a long time, and that the AI/agent work is the reason you want this job. Show the work. A harness, an eval loop, an agent skill pack, or a real open-source contribution we can click. Blog posts about "using ChatGPT" are not proof. This is an engineering seat. You will own production internal AI systems (harness + fraud/CS back office) and accelerate the eng team. Not DevRel. Not enablement theater. Not a prompt intern. About the Role Truss is a ~dozen-person YC fintech (S21) building banking and payments for construction. Real accounts, instant ledgering, money movement that has to be right. We already run on AI in two places, and both need a full-time owner. 1. Internal AI systems that are already in production. We have started an internal AI harness. Our fraud and customer-support back office is built with AI: case work, review, the ops surface, not a chatbot bolted on the side. That system needs someone who maintains it, extends it, and treats it like a product, every day. 2. The engineering factory. The team already ships in Cursor / Claude Code. Setup is uneven. Venvs, VMs, skills, and repos drift. Skills rot. We do not have a daily picture of what shipped or how good the code is. You make every engineer dangerous in the same way. You sit on the engineering team as a forward-deployed AI engineer. Embedded with the people doing the work. You own the harness and the back office as real software. You also own environments, team skills, repo contracts, quality scrape jobs, and process so the factory gets tighter every week. If you only want to write product features, stop. If you only want to do "AI enablement" and will not own the back office, stop. If you want to build and run the AI layer the company already depends on, keep reading. What You'll Actually Do Own the internal AI harness. It exists. You maintain it, harden it, and keep building it full time. Evals, tool wiring, sandboxes, whatever makes agents reliable here. Own the AI back office. Fraud and customer support already run on an AI-built back office. You extend it, keep it correct, and sit with ops when a case flow is wrong. This is production, not a demo. Make every engineer's environment actually work. Cursor, agent runtimes, virtualenvs, VMs, secrets, language toolchains. When someone clones a repo, the agent can boot, test, and ship. You notice when it cannot. Own team skill sets. Shared skills for our stack and our way of working. You keep them current. Stale skills are a bug you fix, not a wiki someone might read. Set the repos up for agents, not just humans. Devcontainers or equivalent, task runners, test entrypoints, lint, fixtures, docs the model can follow. The factory has a contract. Build jobs that scrape the work. Daily signal on what shipped, review quality, test health, agent usage, where we are slow or sloppy. Improve the factory. Do not build a vanity dashboard. Install process that sticks. How we open PRs, how we review with agents, how we promote a skill, how we kill a bad one. Boring, enforced, light. Sit with the people who use it. Engineers and ops. You watch a session or a case fail and fix the harness, the skill, or the back office. You do not throw a Notion page over the wall. What We're Looking For 6+ years as an engineer, shipping production software. Languages and stacks matter less than: you have been in…
Listing via Findwork.