Machine Learning Engineer

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.

• →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.