AI Engineer

Who are we?

Gain is the Private Markets Super App, a connected global platform transforming how leading investors, advisors, and corporates source, evaluate, and execute deals. By combining investment-grade intelligence, AI-powered workflows, and proprietary data in one unified platform, Gain enables top deal teams to move faster, reduce risk, and build conviction with confidence.

Gain is trusted across more than $1 trillion of private capital, used by 100% of MBB and Big Four firms, and by 80% of the world’s top 20 M&A advisory houses.

Recognized for fast innovation and impact, Gain has been named both US and EU Data Provider of the Year by PE Wire, awarded Global Financial Market Review’s Best Use of AI in Finance, and ranked in Sifted’s Top 100 Fastest-Growing Companies, the Forbes 1000, and the Deloitte Technology Fast 50. Gain operates globally with offices in New York, London, Amsterdam, Frankfurt, Warsaw, and Bangalore.

How we work

• Predicting what isn't published. Many private companies publish little or no financial detail. Across a database of more than 5 million companies, we predict revenue and EBITDA from sparse labels, and we predict whether and when a company will be bought or sold, which is a time-to-event problem with censored data.

• Agents on a messy web. Our agents search, navigate and extract company data points from sources that contradict each other, then decide which source to trust before committing to an answer.

• Quality that holds while everything underneath moves. We're provider-agnostic. Model choices follow the evals, and we switch LLMs when the numbers say so. That only works if the evals are trusted.

What will you do?

The work is roughly half LLM agents and half custom ML, leaning slightly toward agents.

• The design of our agent pipelines: architecture, tools, and which model for which task. You'll shape the technical direction with our AI Lead.

• The evaluation behind agents and models: offline reference sets, live production evals, and the call on whether an improvement is real.

• Our prediction models for financials and deals: features, validation, retraining, and knowing when a simpler model wins.

• Getting work into production: trace where pipelines fail and fix the weakest links first, with compute, memory and model cost in view.

• Raising the bar in the team through design reviews, pairing, and an eval-first habit that sticks.

In your first six months you'll own the evals for our agent pipelines, propose where we invest next, and ship measurable improvements to both the agents and the prediction models.

Who are you?

• You've built and owned ML systems in production for years, typically 6+ in ML or software engineering.

• In the last 1–2 years you've shipped LLM agents or pipelines that real users depend on, not demos.

• You have modelling judgement. You've dealt with leakage, distribution shift and calibration on real data.

• You measure before you claim. You can build an evaluation set and tell an honest improvement from a lucky one.

• You care about the user outcome more than the technique. Ideas without a clear customer benefit don't get built here, however interesting.

• Nice to have: time-to-event modelling, noisy or conflicting data sources, financial or private-company data. No finance background needed.

The team

You'll join our Data & AI team of six and report to Renan, our AI Lead who's been building production models for >10 years. Our engineering team runs the infrastructure, and you shape what runs on it.

The stack

• Core: Python (async, fully typed with Pydantic) and FastAPI

• LLMs: multiple providers behind our own provider-agnostic layer, with Pydantic AI for typed agents.

• Evals and observability: Pydantic Evals and Logfire

• Data: PostgreSQL with pgvector

• Cloud: GCP (Cloud Run, Cloud Tasks), Docker, GitHub Actions

What do we offer?

• Competitive base salary and annual bonus linked to your performance / OKRs

• Attractive benefits including health & wellbeing allowance, work-from-home budget, learning & coaching benefits, etc.

• Flexible hybrid working model with 3 days per week in our Amsterdam office

• Healthy work-life balance allowing for planability and personal commitments

• Chance to grow with the company gaining increasing responsibilities, supported by lots of coaching and a feedback-driven approach

• Tremendous learning and career progression opportunities

• International environment with hubs in New York, London, Amsterdam, Frankfurt, Warsaw, and Bangalore

• Culture of trust, ownership and standard of excellence and a fun working atmosphere with regular outings and events

• Post product-market fit and aspiring unicorn status - this is an excellent time to join & grow with us!

Does this sound like a perfect match?

We are proud of our wonderful product and believe it has lots of potential. We are growing fast and having fun while building our platform and company. Does this sound like a team you would like to become a part of? Click ‘Apply’ and submit your CV, we are excited to get to know you and show you what we do!

Gain.AI B.V., with its registered seat in Apollolaan 151, 1077 AR Amsterdam, The Netherlands, or another Gain.AI or Gain.pro entity which runs the recruitment process, further called „Gain", is the controller of personal data you submit as part of the job application. Your personal data will be processed for the purpose of conducting a recruitment process for the job position you apply for. If you provide Gain with explicit consent, your personal data will also be processed for the purpose of conducting future recruitment processes. Please read the Gain Privacy Policy to acquaint yourself with how Gain processes your personal data: https://www.gain.ai/privacy-and-cookie-statement.

Gain is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, national origin, disability, veteran status, or any other legally protected characteristics.

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