Prodigal is the connected AI platform leading financial institutions use to run their operations.
We work with banks, lenders, credit unions, and other financial companies that lend money to people and manage those relationships over time.
These institutions make millions of high-stakes decisions every day. Who should they reach? When should they reach them? What should they say or offer? When should a case move to a human? How should that change based on the borrower, the account, previous interactions, and the regulations involved?
Getting those decisions right requires a deep understanding of the people, processes, rules, and edge cases behind them.
Prodigal has spent the last eight years building that understanding. More than a billion interactions between financial institutions and their customers have shaped the intelligence, guardrails, and AI agents we now run in production across North America.
Today, our AI agents analyze conversations, capture context, guide human agents, decide the next action, conduct customer conversations, orchestrate outreach, and help people complete payments and resolutions. They are connected, so what is learned in one interaction can inform what happens next.
We are expanding this swarm of AI agents across more of the work financial institutions do: originations, document processing, back-office workflows, servicing, and other critical operations where money, identity, people, and regulation intersect.
We are backed by Y Combinator, Accel, and Menlo Ventures, and work with 100+ financial institutions across North America. Listen directly from our CTO, Cofounder - Sangram Raje
The Data Science and Machine Learning (DSML) team owns the full lifecycle of Prodigal's ML systems, from data curation to model selection, fine-tuning, and post-training, through to deployment, evaluation, and ongoing monitoring. We sit close to real product and customer problems, and we're expected to move from experiment to production with both speed and rigor.
What You'll Do- Understand the problem domain. Work closely with product, engineering, and customer-facing teams to turn ambiguous, real-world problems into well-scoped ML problems.
- Curate datasets. Source, clean, and structure the data needed to train and evaluate models, often before any "off-the-shelf" dataset exists.
- Train models across the full spectrum. Build and train everything from simple classifiers to neural networks, transformers, and small/large language models, choosing the right level of complexity for the problem at hand.
- Deploy and scale for inference. Take models from notebook to production. optimizing for latency, cost, and reliability at scale.
- Evaluate and close the loop. Design evaluation frameworks that actually measure what matters, and build learning loops.
- Strong fundamentals in ML/DL, backed by hands-on experience training and shipping models, not just coursework or competitions.
- High agency, you take an ambiguous problem and run with it rather than waiting for it to be fully specified.
- A first-principles approach to problem-solving – you reason from the ground up rather than defaulting to whatever's fashionable or familiar.
- Genuine curiosity and the comfort to operate at the edge of what you already know.
- Real problems, real models, real data. No toy datasets, no academic benchmarks — you'll work on problems that directly affect customers and the business.
- Frontier technology, applied. Learn to harness the latest in AI and put it to work on problems that matter.
- Smart people, fast shipping. Work alongside a sharp team, ship quickly, and grow faster than you would elsewhere.
- 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.
Health insurance for you and your family, meals at office on us, travel reimbursement, unlimited leaves, gym membership, unlimited learning & development, flexible work schedule and a world-class team to learn and grow with!
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.
