Senior Machine Learning Engineer
- Location
- San Francisco
- Workplace
- On-site
- Compensation
- $250k – $300k + equity
About this role
What You’ll Do
Ownership is everything here. You'll be responsible for our core ML detection platform, and the systems you build are what separate Petra from every other player in this space.
Petra is a premium 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 us and second place.
Concretely, you will
Build and evolve our detection models. Working closely with our CTO and Head of Machine Learning, 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. We're 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 we build. You'll establish patterns, push for correctness, and raise the quality of everything around you.
Our stack is Node, TypeScript, React, and Python... for now.
Who You Are
4+ years of machine learning engineering experience, with meaningful time shipping models in production systems at scale.
Strong quantitative fundamentals: probability & statistics, linear algebra, anomaly detection, behavioral modeling, NLP.
Experience training & deploying models in production: You know how to pick the right model for the job. 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
We're small and high-caliber. Our founders are ex-Abnormal Security, Palo Alto Networks, and Bridgewater. Other early team members include:
A Sequoia-backed founder
An engineering leader from Anduril
Quants from Citadel and Point72
You'll work directly with people who are exceptional at what they do.
What happens next
Skip the application pile. I get you in front of the people who decide.
Confirm the fit
A few questions to make sure this role is the right shape for you. Two minutes.
I pitch you to the company
I write the intro, send it to the founder, and handle the back-and-forth.
A meeting lands on your calendar
When the company wants to meet, I get the call on your calendar. You just show up.
Culture & values
Feedback-first culture: people are direct, give feedback constantly, and expect the same in return.
Collaborative environment with youthful energy, with the team mostly in their early-to-mid 20s.
People take their work seriously but don't take themselves too seriously.
High-output, fast-moving startup where everyone contributes to building from zero to one.
In-office energy: Bay Area team members are in the San Francisco office five days a week, fostering tight collaboration.
Small, high-performing team building something extraordinary.
Zero-to-one building: you'll be shaping processes, tools, and strategy alongside the founding team, not inheriting a rigid playbook.
Know someone who'd be great for this?



