About the Role
This is a senior individual contributor role at a fast-growing cybersecurity and account-integrity organization. The engineer who steps into this seat owns the core ML detection platform end to end, from research and training through live production systems that identify account compromise in real time. The work is high-stakes, high-autonomy, and directly tied to product quality in a space where technical edge is the competitive moat.
The ideal candidate has 4 to 10 years of ML engineering experience with a clear record of shipping models to production at scale, ideally at a top-tier ML organization. They bring strong quantitative fundamentals, real-time pipeline experience, and the kind of high-agency mindset that means they scope their own work, move fast, and raise the bar for everyone around them. A background in quant finance or fraud detection, and a first-author publication at a top ML venue, are meaningful differentiators here.
Key Responsibilities
1. Design, build, and continuously improve real-time ML detection models that identify account compromise at scale
2. Own the full model lifecycle from research and training through deployment, monitoring, and iteration in live production systems
3. Architect streaming data pipelines, feature stores, and high-performance database integrations that can handle 10 to 100x growth within months
4. Partner directly with senior technical leadership to set the technical direction for the ML platform
5. Establish engineering patterns, review code, and raise the quality and correctness of the system as a senior voice on a lean team
6. Talk with customers to understand the threat landscape and translate those insights into new modeling signals
7. Identify the highest-leverage problems, propose solutions independently, and execute without waiting for direction
Required Skills & Experience- 4 to 10 years of machine learning engineering experience with meaningful time shipping models to production in Python
- Demonstrated end-to-end ownership of ML systems at scale at a reputable ML organization
- Strong quantitative fundamentals including probability, statistics, linear algebra, anomaly detection, and NLP
- Production-grade Python coding skills and comfort owning a system beyond just the modeling layer
- Experience with real-time or streaming data pipelines, feature stores, distributed systems, and high-performance databases
- BS or higher in Computer Science, Mathematics, Statistics, or a quantitative field
- High agency: self-directed, fast-moving, and always expanding scope without being asked
Preferred Skills- Background in quantitative finance or fraud detection
- First-author publication at a top ML conference such as NeurIPS, ICML, or ICLR
- Experience working in high-stakes, low-latency production environments with real adversarial conditions