Start Date
Immediate
Expiry Date
11 Jul, 25
Salary
0.0
Posted On
11 Apr, 25
Experience
5 year(s) or above
Remote Job
Yes
Telecommute
Yes
Sponsor Visa
No
Skills
Predictive Modeling, Forecasting
Industry
Information Technology/IT
ABOUT STACKS
At Stacks, we’re transforming how finance teams approach one of their most critical processes: the monthly close. For mid to large enterprises, this process is often manual, repetitive, and time-consuming - diverting finance teams from strategic initiatives to focus on tedious tasks. We believe it’s time for change.
Our vision is bold: with the power of AI, we aim to make the monthly close as simple as a single click - delivering precise financial insights on day one of each month. By freeing finance teams from low-value tasks, we empower them to focus on strategic work that truly drives business growth.
Our team is a blend of finance, product, and technical experts from top-tier companies like Uber, Plaid, Miro, Mollie, and Bunq, united by the drive to build a game-changing solution. Based in the heart of Amsterdam, our office offers inspiring canal views and a collaborative atmosphere. Backed by leading VCs and executives from Stripe, Plaid, and OpenAI, we’re poised to reshape the future of finance.
5+ YEARS OF EXPERIENCE
Background as a Data Scientist, Data Engineer, or ML Engineer, with a history of delivering high-impact machine learning solutions.
ABOUT THE ROLE
We’re looking for a Staff AI Engineer to spearhead our AI and ML efforts. This is far from a typical engineering role - it’s an opportunity to build our machine learning and data function from the ground up. Financial data can be a goldmine that’s often buried and untapped, and we need someone with the expertise and tenacity to unlock its full value.
You will be at the forefront, developing cutting-edge solutions to automate financial close workflows and surface key strategic insights. Beyond tackling fascinating machine learning challenges, you’ll also handle the foundational work - designing robust data pipelines and transforming messy, unstructured data into clean, valuable assets. If you’re passionate about diving deep into data, revel in both the glamorous and gritty aspects of ML, and want to make a significant impact on our product and company trajectory, this role is for you.