Bring Your Own Engineering Team
Fractional Ai
| Company | Fractional Ai |
| Category | Uncategorised |
| Location | San Francisco |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 29 Nov 2024 |
| Last verified | 2 Aug 2026 |
| Source | Employer career page (ashby) |
Description
ABOUT ODE WITH ANTHROPIC
How do you turn a decades-old dataset into an industry-leading medical coding agent? Teach an AI receptionist to book appointments in Spanish? Automatically generate working data connectors from API docs? Build agents that write, test, and deploy their own software packages?
Anthropic is defining frontier AI. Ode is the team bringing frontier AI into practice.
We are a team of experienced builders who care deeply about getting complex AI systems into production, with strong conviction about what makes them succeed. As Anthropic’s services venture, we tackle the hardest, most consequential AI problems for companies with real stakes, working closely with their Applied AI and Product teams to turn frontier technology into real world impact.
We're backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs, General Atlantic, Leonard Green & Partners, Apollo Global Management, GIC, and Sequoia Capital.
We're headquartered in SF with offices in NYC, Raleigh-Durham, and Dubai.
ABOUT BRINGING YOUR OWN ENGINEERING TEAM
Some teams are just too good to break up. Maybe you co-founded a venture that's pivoting, maybe your team got caught in a reduction in force, or maybe you're looking for the next thing to build together. We already work in small pods: tight-knit teams of engineers working on a single project together. Joining Ode is a natural way to keep your team intact and shift your focus to building for clients with instant product-market fit. If you're part of an existing team of 2–5 engineers, we encourage you to apply to join Ode with Anthropic together.
HOW IT WORKS
- One interview process. We'll arrange your interviews on the same day, with the same panel, against the same bar we hold every Ode engineer to.
- One offer. If we decide to move forward, we'll make offers to your whole team at the same time. You're each free to accept or decline individually — though we hope you'll all say yes.
- One first project. We'll work to staff you on an engagement together.
BYOT IN PRACTICE: FABIUS
In 2026, we were introduced to the Fabius team — a Y Combinator (W23) startup that had spent three years building AI to automate sales workflows. They had a track record of building complex AI systems, and they'd spent considerable time perfecting the craft of partnering closely with customers to make sure their solutions delivered results. The more we talked, the more we saw a team whose strengths mapped almost exactly to what we hire for. For Fabius, joining Ode was a natural way to focus on what they were best at — building AI-powered products — while staying together as a team.
"I was surprised how much autonomy Ode expected us to keep. Suddenly we had very interested, very motivated clients — and the same hard technical problems we used to grind through at Fabius. " — Neil Madsen, Fabius Co-founder
"When we spoke with Eddie and Chris, it felt like a bunch of founders hanging out. There was a certain expectation that the team itself was an asset, and Ode was specifically interested in protecting the way we were working together, rather than figuring out how to make everyone play a narrow role." - Andy Day, Fabius Co-founder
WHO WE HIRE
We generally expect your team to fall into the categories of one of our existing engineering and PM roles — most commonly Software Engineer, Senior/Staff Software Engineer or Forward Deployed PM. Take a look at those JDs to see what we look for at the individual level. We evaluate teams holistically on top of that, but we expect every team member to clear a similar bar as all other Ode team members.
- Software Engineer
- Forward Deployed Product Manager
OUR ENGINEERING PHILOSOPHY
We're opinionated about what works in production, and we write about it — from why most eval setups fail by collapsing everything into a single score, to why defaulting to chat interfaces limits AI's impact.
We have a distinct
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