ABOUT ALEPH
Aleph is an AI-native platform for Financial Planning & Analysis (FP&A), an established software category with a multi-billion market but no clear winner. We’re trying to solve a problem many finance teams are super familiar with: data scattered across a million systems, endless spreadsheets and way too much time spent getting numbers to line up instead of actually using them to make decisions.
Aleph was founded by Albert Gozzi and Santiago Perez De Rosso, two technical founders with backgrounds from Stanford and MIT and experience working at top-tier companies such as Google, Microsoft and Bain & Company. We’re backed by top VCs (Khosla Ventures, Bain Capital Ventures, YC, Picus Capital), and work with customers like Webflow, Notion, Zapier, Y Combinator and many others.
We are hiring remotely across the Americas (United States, Canada, LATAM).
THE CHARTER
Every AI feature at Aleph - asking questions of your financials, agents that do full FP&A workflows - runs on a shared foundation: which model handles which task, what context the model sees, what tools it can call, and how we measure whether it's getting better. Today those decisions live inside product teams. This role owns them as a whole, including the model proxy and eval infrastructure everything runs through.
WHAT YOU'LL DO
- Own the AI foundation product teams build on: model selection and routing, the model proxy layer, context management, and tool design
- Own eval infrastructure. Work with customers and finance domain experts to define what excellent output looks like, then encode it in evals every team can run - in FP&A, a wrong answer ends up in someone's board deck
- Ship agentic features end to end, from v0 through the optimization loop: prompt and context engineering, caching, parallel tool calls, subagent patterns
- Build observability into agent behavior so we can profile it, find bottlenecks, and decide what to fix with data
- Track what's changing in agentic systems and bring the practices that prove out into how we build
WHAT WE'RE LOOKING FOR
- You've shipped production LLM systems - evals, context management, multi-model or multi-provider routing - and can talk concretely about what broke and what you changed
- You build proof of concepts, decide quickly, and ship v0s. Evals drive your quality bar
- Strong judgment about abstractions: opinionated about design, pragmatic about shipping incrementally
- Experience with LLM APIs and agent frameworks, and shipped user-facing products
- You want to ship production systems, not do research
WHAT WE EXPECT FROM ALL STAFF+ ENGINEERS
- Adoption: teams you don't manage have picked up systems you built - because they were that good
- Strong fundamentals: you can trace requests from UI to data store and make good technical decisions at every layer
- High agency: background as a founder, engineering lead, or startup builder. You have a builder's mindset and find creative ways to ship
- Pragmatism: you choose the fewest and simplest tools that will actually work
- Craft: you care deeply about code quality and write code that's correct and easy to understand
- Communication: clear, direct, and persuasive across technical and non-technical audiences
- Experience: you've built complex B2B products, ideally from scratch
While this is a remote-first opportunity, we're focusing on candidates within the Americas to better align with our working hours as a team.
Aleph is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.
This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. Aleph makes hiring decisions based solely on qualifications, merit, and business needs at the time.