Ownership is everything here. You'll be responsible for the core ML detection platform, and the systems you build are what separate this company from every other player in the space.
This is a premium security product and it's everyone's job to make sure it stays that way. That means you won't just train models - you'll talk to customers, understand the attacks they're facing, and think creatively about how to find the next signal that widens the gap between this team and second place.
Concretely, you will:
Build and evolve detection models. Working closely with technical leadership, you'll design and build the ML systems that identify account compromise in real time.
Take models from research to production. You don't just care about model performance in a notebook. You're energized by measuring how your models behave at scale in a live system with real attackers.
Architect for scale. The company is growing fast. Nothing excites you more than architecting and implementing an elegant solution that'll handle 10-100x the scale in 6 months.
Raise the technical bar. As a senior voice on a lean team, your instincts and opinions shape how the team builds. You'll establish patterns, push for correctness, and raise the quality of everything around you.
The stack is Node, TypeScript, React, and Python.
Who You Are:
4+ years of machine learning engineering experience, with meaningful time shipping models in production systems at scale.
Strong quantitative fundamentals: probability and statistics, linear algebra, anomaly detection, behavioral modeling, NLP.
Experience training and deploying models in production: you know how to pick the right model for the job, and just as importantly, you know the limitations of each modeling approach you consider.
Strong engineering fundamentals: you write clean, production-grade code and are comfortable owning a system end to end, not just the modeling layer.
Experience working with real-time or streaming data pipelines, feature stores, high-performance databases, and distributed systems. Bonus if you've worked in high-stakes domains like quantitative finance or fraud detection.
High agency and high output. You identify the problem, propose the solution, and execute. You're constantly looking for ways to expand your scope and circle of competence.
Sharp and scrappy. You learn fast: you ask the right questions and figure it out. You ship fast: you find the 90/10 and don't let perfect be the enemy of done.
Comfortable in a small-team, high-autonomy environment. The scope of your work is rarely handed to you, and you wouldn't have it any other way.
Proud of your craft. You care about building things that work well and hold up over time.
The Team:
Small and high-caliber. You'll work directly with people who are exceptional at what they do.
- 4+ years of machine learning engineering experience, shipping models to production in Python at scale
- Took ML models end-to-end from training to production at scale at a reputable ML organization
- Built real-time data pipelines or streaming infrastructure
- Strong quantitative fundamentals: statistics, linear algebra, anomaly detection, behavioral modeling, NLP
- Production-grade Python coding and system design ability
- Experience with distributed systems and high-performance databases
- Background in quant finance or fraud detection a plus
- High agency: scopes own work and executes without direction
- BS+ in CS, Math, Statistics, or a quantitative field
- First-author publication at a top ML conference (NeurIPS, ICML, ICLR) a plus