About The Role
Abnormal Security is looking for a Senior ML Infra Engineer to join the Detection Team. The Detection Division is focused on building the world’s most advanced technology for identifying and stopping email and cloud-based attacks that were previously undetectable and help make the world a safer place. As an ML Infra Engineer focused on building systems for Detection’s Signal Platform, you will be responsible for making feature development at Abnormal a fast, responsive, stable, and confident experience for Machine Learning Engineers.
The ideal candidate would have the following qualities:
- A first principles approach to building scalable, customer-centric solutions
- A drive to solve meaningful & pragmatic problems for real-world people
- An ownership and impact oriented outlook on your efforts and growth
- An ability to iterate in real-time-solving novel problems, quickly and autonomously
An ability to iterate in real-time - solving novel problems, quickly and autonomously
What You Will Do
- Architect, design, build, and deploy backend services and infrastructure that support a world-class Detection Engine
- Ownership projects that enable us to meet ambitious goals for, such as building the plan to scale components of Detection’s ML Platform, such as our Behavioral Aggregate Systems, by 10x
- Collaborate closely with MLE teams, by distilling feedback, correlating it to our strategy, and executing on projects that solve key customer pain points
- Coach and mentor junior engineers via high quality code reviews and design reviews
Must Have
- 5+ years of professional experience as a hands-on engineer building data-oriented products and/or ML systems/products
- Experience with real-time, online, and/or high-throughput & low-latency distributed systems
- Knowledge of key ML Ops team technologies (Spark, Data platform and Data coordination, Hadoop, Hive, feature platform serving systems, ML training and ML serving platforms, etc.)
- Knowledge of key ML Ops team technologies (Spark, Data platform and Data coordination, Hadoop, Hive, feature platform serving systems, ML training and ML serving platforms, etc.)
- Works well with other stakeholders - has worked with cross-functional teams to drive projects over the finish-line.
- High standards - sets high standards and expectations for project execution for themselves and for collaborators
Nice to Have
- MS degree in Computer Science, Electrical Engineering or other related engineering field
- Experience with big data or statistics
- Experience in ML development
- Familiarity with cyber security industry
#LI-ML1
At Abnormal Security certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons. We know that benefits are also an important piece of your total compensation package. Learn more about our Compensation and Equity Philosophy on our Benefits & Perks page.
Base salary range:
$176,000—$207,000 USD
Top Skills
What We Do
The Abnormal Security platform protects enterprises from targeted email attacks. Abnormal Behavior Technology (ABX) models the identity of both employees and external senders, profiles relationships and analyzes email content to stop attacks that lead to account takeover, financial damage and organizational mistrust. Though one-click, API-based Office 365 and G Suite integration, Abnormal sets up in minutes and does not disrupt email flow.
Abnormal Security was founded in 2018 by CEO Evan Reiser, CTO Sanjay Jeyakumar, Head of Machine Learning Jeshua Bratman, and Founding Engineers Abhijit Bagri and Dmitry Chechik. The team previously built behavioral profiling and machine learning technologies at Twitter, Google and Pinterest that are being applied to solve a problem that costs organizations $1 billion per year, according to the FBI. The Abnormal Security platform stops targeted phishing, business email compromise and account takeover attacks that have never been seen before.
