Machine Learning Engineer, Payment Protection

San Francisco

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Fraud prevention solutions that enable your business to innovate & grow without increased risk. Protect your customers and commerce with Sift fraud solutions.

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Our team helps customers proactively prevent payment fraud. Commerce and financial transactions are increasingly moving online. While the internet has enabled easier and instantaneous payments, it has also introduced a layer of anonymity between the transacting parties. To fully leverage the power of online commerce: there must be mutual trust between parties. Without trust, commerce cannot happen. 

By providing a clear, accurate assessment of risk at multiple points of the end-user journey, we enable merchants to eliminate fraud losses, and create adaptive, frictionless experiences for low risk payments. We believe that machine learning is THE way to empower internet-scale businesses to prevent payment fraud.

What you’ll do

  • Research and apply the latest machine learning algorithms to power our core business product
  • Build offline experimentation systems used to simultaneously evaluate tens of thousands of models
  • Work on maintaining and improving Sift’s ML models and architecture.
  • Scale machine learning pipelines used to produce thousands of models derived from terabytes of data
  • Build systems that automatically explain how a model arrived at a prediction
  • Use data science techniques to analyze fraudulent behavior patterns
  • Collaborate with other teams to build new ways to use machine learning within Sift
  • Generate and execute on ideas to provide customers with meaningful and actionable insights to identify and prevent fraudulent behaviors and transactions
  • Leverage anomaly detection algorithms to identify unusual behaviors for customer traffic patterns

What would make you a strong fit

  • Practical understanding of machine learning and data science concepts, and a track record of solving problems with these methods
  • 2+ years of experience working with production ML systems.
  • 2+ years experience working with large datasets using Spark, MapReduce, or similar technologies
  • 2+ years experience building backend systems using Java, Scala, Python, or other language
  • Experience training machine learning models end-to-end
  • Strong communication & collaboration skills, and a belief that team output is more important than individual output
  • Degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field

Bonus points

  • Experience working with scalable, real-time prediction systems in production
  • Familiarity with multiple machine learning or statistical packages in Python, R, MATLAB, or another programming language
  • Experience evaluating model performance 
  • Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field

A little about us:

Sift is the leading innovator in Digital Trust & Safety.  Hundreds of disruptive, forward-thinking companies like Airbnb, Zillow, and Twitter trust Sift to deliver outstanding customer experience while preventing fraud and abuse.

The Sift engine powers Digital Trust & Safety by helping companies stop fraud before it happens. But it’s not just another anti-fraud platform: Sift enables businesses to tailor experiences to each customer according to the risk they pose. That means fraudsters experience friction, but honest users do not. By drawing on insights from our global network of customers, Sift allows businesses to scale, win, and thrive in the digital era.

Benefits and Perks:

  • Competitive total compensation package
  • 401k plan
  • Medical, dental and vision coverage
  • Wellness reimbursement
  • Education reimbursement
  • Flexible time off

Sift is an equal opportunity employer. We make better decisions as a business when we can harness diversity in our experience, data, and background. Sift is working toward building a team that represents the worldwide customers that we serve, inclusive of people from all walks of life who can bring their full selves to work every day.

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Job region(s): North America
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