At Prodigal, we are building AI Agents for loan servicing and collections. Founded in 2018 by IITB alumni, our journey began when our founders Shantanu and Sangram faced the antiquated the $13B+ lending/collections industry was mostly using pen and paper and decided to build the first real intelligence layer for debt recovery.
Today, we stand at the forefront of a seismic shift in the industry, building Agentic AI applications for loan servicing and collections. Powered by our cutting-edge platform, Prodigal’s Intelligence Engine (PIE), we’re creating the next-generation agentic workforce - one that empowers companies to achieve unprecedented levels of operational excellence and intelligence.
With over 100+ enterprise customers across North America and backing from Y Combinator, Accel and Menlo Ventures, we are the fastest growing AI company in consumer finance.
We're looking for a Machine Learning Engineer to join the PIE team and work on the systems that power our autonomous AI agents in live financial conversations. You'll work alongside a small, high-agency team where you will have real ownership over features and components from day one.
This is a hands-on engineering role. You'll be deep in the code - building ML models, designing the ML system, finetuning and integrating LLMs, tuning prompts, and debugging gnarly real-time issues. You don't need to have done all of this before, but you need to be the kind of engineer who figures things out fast, takes feedback well, and ships.
What You'll Do- Work across a range of ML problems - Voice, Data, Recommendations, Infrastructure, LLMs, Product.
- Train LLMs for AI agents used for consumer finance.
- Develop evals, harnesses, and monitoring systems.
- Own major technical areas with significant autonomy, from problem definition to production deployment
- 2 – 5 years of hands-on engineering experience, with good exposure to building or working with ML or AI systems in production.
- Solid Python fundamentals - you are comfortable writing clean, maintainable code and debugging production issues.
- Some experience working with LLMs: prompt engineering, API integrations, or building simple pipelines or agents.
- High bias for action - you don't wait to be told exactly what to do, and you push yourself to ship rather than over-engineer.
- Eager to learn in a fast-moving environment, take ownership of your work, and ask good questions.
- Real ownership from day one: ML team is a small pod. You won't be a cog - you'll own components, ship features, and see your work in live consumer conversations within weeks.
- Frontier work: AI that reasons, decides, and acts is one of the hardest problems in applied AI. You'll be learning by doing on problems most engineers never touch.
- Strong mentorship: you'll work directly with senior engineers and the ML Lead who will invest in your growth - this is a place to level up fast.
- High leverage early career: the decisions you make and the code you write will impact millions of financial conversations. Rare for an early-career role.
From day 1, Prodigal has been defined by talented, humble, and hungry leaders and we want this mindset and culture to continue to blossom from top to bottom in the company. If you have an entrepreneurial spirit and want to work in a fast-paced, intellectually-stimulating environment where you will be pushed to grow, then please reach out because we are looking to build a transformational company that reinvents one of the biggest industries in the US.
To learn more about us - please visit the following:
Our Story - https://www.prodigaltech.com/our-story
What shapes our thinking - https://link.prodigaltech.com/our-thesis
Our website - https://www.prodigaltech.com/
Skills Required
- 2-5 years of hands-on engineering experience with exposure to building or working with ML/AI systems in production.
- Solid Python fundamentals; ability to write clean, maintainable code and debug production issues.
- Some experience working with LLMs (prompt engineering, API integrations, or building simple pipelines/agents).
- High bias for action; ability to take initiative and ship features without detailed direction.
- Eager to learn, take ownership, and ask good questions in a fast-moving environment.
What We Do
Prodigal is a pioneer of Collection & Servicing Intelligence, a new category of AI software, which enables banks, lenders and collection agencies of all sizes to quickly and efficiently collect accounts receivables. Our cloud-native, Collection & Servicing Intelligence platform delivers actionable insights for banks, lenders, and ARM agencies to maximize revenue, optimize operations, and minimize compliance risk. Prodigal delivers artificial intelligence and machine learning capabilities to lenders and ARM agencies. Prodigal restores value from accounts past due and improves servicing productivity while retaining customer loyalty. We empower entire teams from executive leadership to representatives with data and insights needed to segment and prioritize accounts, to enhance portfolio yield, and to address procedural and legal (TCPA, FDCPA, UDAAP, ...) non-compliance. With our Collection & Servicing Intelligence Platform, senior executives have complete intelligence about expected liquidation, aggregated agent productivity, and FDCPA/TCPA non-compliance in real-time — an imperative for a modern collections business in an increasingly regulated environment. Prodigal is headquartered in the heart of Silicon Valley (Sunnyvale, CA) and is founded by industry veterans with deep expertise in financial services, predictive modeling, speech AI and core engineering. Our investors include top tier investors like Menlo Ventures, Accel andY Combinator. Prodigal has been featured in TechCrunch, American Banker, CBS News, Fortune, insideARM and other leading technology and financial services news sites.
