The role
- COMP
- $250K - $300K
- EQUITY
- Competitive equity
- LOCATION
- San Francisco
- WORKPLACE
- On-site
- EXPERIENCE
- 3 - 8 years
- VISA
- None
- STACK
- Python, NodeJS, TypeScript, React
- INDUSTRY
- Software Development, AI, Cybersecurity, Security
/roles — ROLE_249
Senior Machine Learning Engineer
Accelerator-backed security startup using machine learning to catch compromised business email accounts in real time
The company
Fast-growing AI cybersecurity startup protecting small and midsize businesses from email compromise and account takeover, sold largely through managed service providers.
- STAGE
- Series A-stage
- FUNDING
- $5M raised
- TEAM
- ~15 people
- FOUNDED
- 2023
JD — the work
About the role
You would own the machine learning detection platform at the core of the product, the piece that most sets the company apart from competitors. Beyond training models, you would talk to customers, study the attacks they face, and hunt for new signals that widen the lead over second place, partnering with the CTO and the ML lead. The team is small and strong: founders from established security and finance firms, plus early hires including a former venture-backed founder, a defense-tech engineering leader, and quants from top trading firms.
What you'll do
- Develop and improve the detection models that flag compromised accounts as attacks unfold
- Move models out of notebooks into a live system and track how they perform against real adversaries at scale
- Design systems that can absorb ten to a hundred times today's volume within about six months
- Talk with customers about the attacks they see and find new signals that improve detection
- Shape how the team builds by setting patterns, insisting on correctness, and lifting overall quality
What they're looking for
- 4+ years in ML engineering, including real time spent getting models into large-scale production
- Solid math and stats grounding, including probability, linear algebra, NLP, behavioral models, and anomaly detection
- Judgment about which model fits a problem and the limits of each approach
- Strong engineering habits and willingness to own an entire system, beyond the models themselves
- Hands-on with real-time data flows, feature stores, fast databases, and distributed infrastructure
- High agency, scrappiness, and pride in craft within a small, autonomous team
Nice to have
- Prior work in fraud prevention, trading, or other high-stakes fields