Machine Learning Ops Engineer

Remote

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Sift
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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About the team:

At Sift, we enhance trust and safety in the digital world with our AI driven technology platform. Our products deliver payment protection, ensure content integrity, and protect account defense for businesses around the world. The Machine Learning Platform team exists to enable product teams to construct and operate their production machine learning services as effectively as possible.

The team does this by providing a platform which handles the common needs of the product teams including production system integration, model training, model availability, health and monitoring infrastructure for model serving, and a streamlined model release process.

What you'll do:

As a Model Release Engineer in the Machine Learning Platform team, you will primarily be responsible for training ML models and deploying models. Additionally, you will manage our model training and release pipeline that enables all our product teams to enhance their models. 

Specifically, you will:

  • Champion the model release process by improving existing systems and building tools to make the release of new machine learning models fast, easy, safe, and minimally disruptive.
  • Implement easily maintainable data processing pipelines.
  • Motivate, listen and empathize, and help engineers and data scientists to excel.

What would make you a strong fit:

  • 2+ years of professional software development experience or a degree in CS (or a related field) with 1+ years of experience.
  • Experience solving problems with production systems, and building solutions and automations to prevent them from reoccurring.
  • Experience with programming languages such as Java and Python.
  • Proven experience with automation and knowledge of configuration management tools.
  • Experience with workflow management tools such as Apache Airflow.
  • Experience with Relational and Non-relational databases.
  • Strong debugging, testing, tuning, and problem-solving skills.
  • Strong communication & collaboration skills, and a belief that team output is more important than individual output.
  • Self-starter, with a quick learning curve.

Bonus points:

  • Experience working with data processing technologies for batch processing, such as Apache Spark and MapReduce.
  • Practical knowledge of how to build end-to-end ML workflows.
  • Knowledge of GCP or AWS cloud stack for web services and big data processing.
  • Familiarity with Docker and container clustering technologies like Kubernetes, GKE or AWS ECS.

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.

This document provides transparency around the way in which Sift handles personal data of job applicants: https://sift.com/recruitment-privacy

Job region(s): Worldwide/Anywhere
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