About You
Consider applying to this role if you’re a passionate, first-principles, Engineering leader with experience leading production engineering teams that build scalable distributed systems or high-throughput “online” solutions. Here you’ll use what you picked up while working in Ads (or Search, Recommendation, etc.) in novel & exciting ways - all in the name of making the world a safer place.
Sounds like you so far? Great! You really should consider applying if you’ve found yourself:
- Slogging through a 6 month project just to extract another .001% efficacy out of your Search infrastructure
- Building incredibly performant & world-class platforms, but wondering if “ad tech” is the type of impact on the world you had in mind
-
You’ve defined ML Ops in 7 ways (and drawn a picture) in your last 4 interviews…and you’re pretty sure they still have no idea what you’re talking about
Lastly, you should DEFINITELY apply if:
- You want what you (and your team) do to have a massive & positive impact on the people who use what you create
- You want to lead a team of highly-capable & world-class individuals from diverse backgrounds who are all dedicated to achieving the same mission
- You think a culture of listening to customers empowers the next generation of successes
About the Role
As the Senior Engineering Manager for our Detection Serving & Signals (“DS&S”) team, you and your team will be responsible for the platforms & systems that power our core Detection capabilities.
We protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. That’s what makes our novel behavioral-based approach so…Abnormal.
But that’s what makes the success of this team, and its leader, so massively impactful. As the threat landscape constantly evolves, it is our ability to extract, learn, and iterate will be central to Abnormal’s ability (and our customer’s) to stop the next-generation of attacks.
While knowledge/passion for “security” or “fraud” is a plus, it isn’t a requirement to be a successful leader for this team. Your background will need production leadership experience akin to ML Ops (Feature Stores, Data Pipelines), Online Serving Systems, or with other high-throughput data-centric distributed systems (10-100k+ of QPS).
What you'll do
- Work in collaboration with internal and external customers & stakeholders
- Ownership & operational leadership over the teams responsible for:
- The infrastructure supporting our core detection capabilities
- The online and offline signals platforms
- The online model serving systems
- The model training & feature stores for existing and future products
- Building foundational capabilities of the systems and signal processing to enable 10x scaling 10x faster
- Leading, maintaining, and growing a team of Engineers to support these efforts
Must have Skills
- 5+ years of professional experience as a hands-on engineer (either MLE or SWE) building data-oriented products and/or ML systems/products
- 2+ years of managing production ML Ops (preferred), or ML-Adjacent Platform teams
- (Nice-to-have) Experience running an Feature platform that powers multiple ML-based products, and ability to guide a team technically in this respect
- 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.)
- Mentor & team amplifier - Has led teams of platform engineers and helped build out systems that make ML engineers 10x more effective.
- Customer obsessed: Working with our internal engineers to determine the systems and signals roadmap and balance against engineering needs. Meeting with customers to understand their needs and explain our engineering roadmap.
- Recruiting magnet - you are able to set up a hiring plan, attract senior engineers, outline the vision of the team and build out a large team.
- Knowledge of best practices across the ML Ops community
- High standards - sets high standards and expectations for project execution for themselves and the whole team.
Base salary range: 203,500 - 234,100
Level: M4
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 and perks page.
#LI-ML1
