AI Engineer

Job description

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 We build AI for our customers, not for its own sake. Every project starts from a clear benefit to the people using Gain, and ideas without one don't get built, however interesting the technology. If that's how you like to work, you'll fit in well. Model choices follow the evals. We don't commit to one LLM provider; we measure, and use whichever model gives our customers the best results. What will you do? The work is roughly half LLM agents and half custom ML, leaning slightly toward agents. Build and improve agents that find company data points on the web using search, page navigation and structured extraction tools, and that weigh conflicting sources before committing to an answer. Develop, improve and retrain the models that predict company financials such as revenue and EBITDA, and the likelihood and timing of buy and sell events. Design and maintain evaluation suites, both offline against reference sets and live in production, so we can measure quality and switch between LLM providers to balance output quality and cost. Ship your work into our production services, trace where pipelines fail, and fix the weakest links first. Design with resources in mind. Our engineering team runs the infrastructure, but you choose solutions with their compute, memory, bandwidth and model costs in view. By month six you'll know each of our agent pipelines end to end, how to evaluate them and where they most need improving. You'll also…