Senior Machine Learning Engineer, Abnormal Security

Behavioral Security Products

Salary not provided
Python
Spark
NumPy
Pandas
Scikit-Learn
Zoom
Senior level
Remote in UK
Abnormal Security

Cloud email security platform

Open for applications

Abnormal Security

Cloud email security platform

501-1000 employees

B2BArtificial IntelligenceSaaSCyber SecurityCloud ComputingFraud

Open for applications

Salary not provided
Python
Spark
NumPy
Pandas
Scikit-Learn
Zoom
Senior level
Remote in UK

501-1000 employees

B2BArtificial IntelligenceSaaSCyber SecurityCloud ComputingFraud

Company mission

To make the cloud a safer place for businesses.

Role

Who you are

  • You are someone who wants to make an impact
  • You are passionate about solving customer problems and have a burgeoning set of skills around machine learning, software engineering and data science with which to do so
  • You want to apply those skills on a problem that leaves the world in a better place
  • You are humble and want to learn!
  • This is one of your first jobs - maybe your actual first job - and you know that there’s a ton of skills to build and knowledge to grow and you want to do so as fast as possible
  • You ask questions. You take notes
  • You have an active and curious approach to your work and as a result you grow faster than the average person
  • Strong software fundamentals - has ample experience coding in a production environment
  • Experience designing systems with data at their center - has thought through and executed on long-term designs to support a scaling product
  • Experience with production ML systems - understands the pillars of a modern ML stack and the development/maintenance process of ML models
  • Fluent with Python and machine learning libraries like numpy and scikit-learn
  • Familiarity with using data processing frameworks like Pandas and Spark
  • Systematic approach to debug both data and system issues with ML models or heuristics
  • Writing code that is easily testable and understood by other engineers
  • Works well with other stakeholders - has worked with cross-functional teams to drive projects over the finish-line
  • Machine learning academic background (Bachelor's degree in Computer Science or related fields)
  • Hands on experience training and tuning models
  • 5+ years of experience applying these skills in a production environment
  • Interest in security and stopping bad actors

Desirable

  • Experience with tuning a machine learning system in a production setting
  • Master’s in Computer Science or related field
  • Experience working in a startup environment
  • Familiarity with LLMs

What the job involves

  • We’re looking to add a Senior Machine Learning Engineer to our Account TakeOver (ATO) Detection team
  • The ATO Detection team’s mission statement is to provide world-class attack detection efficacy to tackle the fast evolving attack landscape using a combination of generalizable and auto-trained models as well as specific detectors for high value attack categories
  • This team is solving a multi-layered detection problem - from modeling communication patterns to establish enterprise-wide baselines, to normalizing across multiple event sources, to making use of contextual information to avoid false positives (e.g. comparing unusual sign in locations with travel records)
  • This role straddles the line between velocity and excellence
  • We are also not only a remote team, but a very distributed team, as such you will need to have excellent communication skills across both verbal and written mediums
  • You will need to be just as comfortable on a zoom call as writing a 1 pager project proposal to be shared across the team for technical feedback
  • Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an ATO detection product, with guidance from senior engineers
  • Build attack detection systems capable of highlighting rare, suspicious activity (one in a million) with 95%+ precision & <1 minute latency on the event stream
  • Understand the nature of attacks and design features to calibrate behavior across our customers from multiple industries, with different usage patterns to provide consistent performance
  • Write code with testability, readability, edge cases, and errors in mind, such as feature drifts between online/offline data
  • Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises
  • Participate in building a world-class detection engine across all layers - data quality, feature engineering, model development, experimentation and operation
  • Work with infrastructure & systems engineers to develop the right feature aggregates to feed into the detection system
  • Create a magical work environment with colleagues and memorable interview process for candidates

Otta's take

Theo Margolius headshot

Theo Margolius

COO of Otta

Fraud involving impersonation is one of the top causes of online financial crime. Criminal tactics like email account spoofing, where the criminal impersonates an official account to steal personal information or money, are rife. Abnormal Security is a startup aimed at handling these hyper-targeted and personalized email attacks by analyzing communications and identifying potential fraud before it can take place.

The fraud detection space is extremely competitive but Abnormal Security differentiates itself through its focus on the threat of impersonation rather than a spectrum of threats. This has allowed it to amass a wealth of data relating specifically to high-risk impersonation attacks, analyzing over 45,000 signals to detect any anomalies.

Its specialized approach has fueled rapid growth, leading to a $4B valuation after a Serice C Funding round. Now, Abnormal plans to double down on product development and expand internationally, prioritizing markets where data security laws necessitate a local presence. By staying focused on impersonation, Abnormal Security positions itself as a formidable force in the fight against online financial crime.

Insights

Top investors

Few candidates hear
back within 2 weeks

11% employee growth in 12 months

Company

Funding (last 2 of 3 rounds)

May 2022

$210m

SERIES C

Nov 2020

$50m

SERIES B

Total funding: $284m

Company benefits

  • Healthcare
  • Flexible PTO
  • 401k
  • One Medical
  • Flexible Spending Account
  • Mental Health Resources
  • Home Office Stipend
  • Monthly Internet & Phone Stipend
  • Health and Wellness Stipend

Company HQ

Yerba Buena, San Francisco, CA

Founders

Having started their career as a Software Engineer, co-founded GamerNook.com, Bloomspot, and Adstack before spending 3 years at Twitter. Co-founded Abnormal Security in April 2018, and has been CEO since.

Previously Senior Software Engineer at Twitter and Google. Was also Software Architect at TellApart.

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