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Prodigal is building AI agents for loan servicing and collections, founded in 2018 by IIT Bombay alumni. The company has grown to serve 100+ enterprise customers across North America and is backed by Y Combinator, Accel, and Menlo Ventures. They are developing Prodigal's Intelligence Engine (PIE), a cutting-edge platform powering agentic AI applications in consumer finance.
As a Machine Learning Engineer on the PIE team, you will work on systems powering autonomous AI agents in live financial conversations. This is a hands-on role where you'll own features and components from day one, working alongside a small, high-agency team.
Key responsibilities include:
- Working across diverse ML problems: voice, data, recommendations, infrastructure, LLMs, and product
- Training and fine-tuning LLMs for AI agents used in consumer finance
- Developing evaluation frameworks, testing harnesses, and monitoring systems
- Owning major technical areas with significant autonomy from problem definition through production deployment
- Building ML models, designing ML systems, integrating LLMs, tuning prompts, and debugging real-time issues
You should bring 2–5 years of hands-on engineering experience with exposure to building or working with ML/AI systems in production. Strong Python fundamentals are essential, along with experience working with LLMs (prompt engineering, API integrations, or building pipelines/agents). The ideal candidate has a high bias for action, doesn't wait to be told exactly what to do, and prioritizes shipping over over-engineering. You should be eager to learn in a fast-moving environment, take ownership, and ask good questions.
This role offers real ownership from day one—you won't be a cog but will own components, ship features, and see your work in live consumer conversations within weeks. You'll work on frontier AI problems (reasoning, decision-making, and action), receive strong mentorship from senior engineers and the ML Lead, and have high leverage as an early-career engineer impacting millions of financial conversations.
