About the Role
<p style="text-align:justify;"><strong><span style="color:#000000">Who are we?</span></strong></p><p style="text-align:justify;"><span style="color:#000000">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.</span></p><p style="text-align:justify;"><span style="color:#000000">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.</span></p><p style="text-align:justify;"><span style="color:#000000">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.</span></p> <br> <p><strong><span style="color:#000000">How we work</span></strong></p><ul><li><p><strong><span style="color:#121317">Predicting what isn't published.</span></strong><span style="color:#121317"> 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.</span></p></li><li><p><strong><span style="color:#121317">Agents on a messy web.</span></strong><span style="color:#121317"> 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.</span></p></li><li><p><strong><span style="color:#121317">Quality that holds while everything underneath moves.</span></strong><span style="color:#121317"> 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.</span></p><p style="min-height: 1.7em;"></p></li></ul><p style="text-align:justify;"><strong><span style="color:#000000">What will you do?</span></strong></p><p><span style="color:#121317">The work is roughly half LLM agents and half custom ML, leaning slightly toward agents.</span></p><ul><li><p><span style="color:#121317">The design of our agent pipelines: architecture, tools, and which model for which task. You'll shape the technical direction with our AI Lead.</span></p></li><li><p><span style="color:#121317">The evaluation behind agents and models: offline reference sets, live production evals, and the call on whether an improvement is real.</span></p></li><li><p><span style="color:#121317">Our prediction models for financials and deals: features, validation, retraining, and knowing when a simpler model wins.</span></p></li><li><p><span style="color:#121317">Getting work into production: trace where pipelines fail and fix the weakest links first, with compute, memory and model cost in view.</span></p></li><li><p><span style="color:#121317">Raising the bar in the team through design reviews, pairing, and an eval-first habit that sticks.</span></p></li></ul><p style="min-height: 1.7em;"></p><p><span style="color:#121317">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.</span></p><p style="text-align:justify;min-height: 1.7em;"></p><p style="text-align:justify;"><strong><span style="color:#000000">Who are you?</span></strong></p><ul><li><p><span style="color:#121317">You've built and owned ML systems in production for years, typically 6+ in ML or software engineering.</span></p></li><li><p><span style="color:#121317">In the last 1–2 years you've shipped LLM agents or pipelines that real users depend on, not demos.</span></p></li><li><p><span style="color:#121317">You have modelling judgement. You've dealt with leakage, distribution shift and calibration on real data.</span></p></li><li><p><span style="color:#121317">You measure before you claim. You can build an evaluation set and tell an honest improvement from a lucky one.</span></p></li><li><p><span style="color:#121317">You care about the user outcome more than the technique. Ideas without a clear customer benefit don't get built here, however interesting.</span></p></li><li><p><span style="color:#121317">Nice to have: time-to-event modelling, noisy or conflicting data sources, financial or private-company data. No finance background needed.</span></p></li></ul><p style="min-height: 1.7em;"></p><p><strong><span style="color:#121317">The team</span></strong