Machine Learning Engineering Manager

Who are we?

Hi! We are Ravelin! We're a fraud detection company using advanced machine learning and network analysis technology to solve big problems. Our goal is to make online transactions safer and help our clients feel confident serving their customers.

And we have fun in the meantime! We are a friendly bunch and pride ourselves in having a strong culture and adhering to our values of empathy, ambition, unity and integrity. We really value work/life balance and we embrace a flat hierarchy structure company-wide. Join us and you’ll learn fast about cutting-edge tech and work with some of the brightest and nicest people around - check out our Glassdoor reviews.

If this sounds like your cup of tea, we would love to hear from you! For more information check out our blog to see if you would like to help us prevent crime and protect the world's biggest online businesses.

The Team

You will be joining the Detection team, a team of machine learning engineers and data scientists. The Detection team is responsible for keeping fraud rates low by continuously training and deploying machine learning models. We aim to make model deployments as easy and error-free as code deployments. Google’s Best Practices for ML Engineering is our bible.

Our models are trained to spot multiple types of fraud, using a variety of data sources and techniques in real time. The prediction pipelines are under strict SLAs; every prediction must be returned in under 300ms. When models are not performing as expected, it’s down to the Detection team to investigate why.

The Detection team is core to Ravelin’s success. They work in a deeply collaborative partnership with the Data Engineering team to design the data architecture and infrastructure that powers our ML systems.

The Role

We are currently looking for an Engineering Manager to line manage ML engineers and drive innovation across our suite of fraud detection products. You’ll work closely with data scientists, product, engineering and our operations teams to develop ML models and ML products. Our ideal candidate is pragmatic, approachable and filled with knowledge tempered by past failures.

You will hire, coach and develop a talented machine learning team to deliver on the ML product and platform roadmap. You are in your element working with people. You’ll partner closely with senior members of Detection to discover new avenues for ML product innovation. From time to time, you’re excited to even do some software or model development yourself.

The work is not all green field research. The everyday work is about making safe incremental progress towards better models for our clients. The ideal candidate is willing to get involved in both aspects of the job – and understand why both are important.

Key Responsibilities

• Act as a key leader in establishing excellence within machine learning engineering and across the detection team
• Line manage a team of machine learning engineers - providing coaching and guidance in support of the ongoing development and growth of your team
• Propose and champion new machine learning methods and tools (including platforms that empower Data Science experimentation) to influence the technical roadmap and promote continuous innovation
• Drive cross-functional initiatives with Data Engineering, Infra, and other teams to align on data architecture and ensure we make the right ML product decisions
• Investigate model performance issues
• Develop and deploy new models to detect fraud whilst maintaining SLAs

Skills, Knowledge and Expertise

• Minimum of 1 year of experience as an engineering manager, managing at least 3 people
• You are a strong collaborator with colleagues outside of your immediate team, for example with data science, engineering, product, client operations teams and with senior leadership
• You know how to manage and retain talented engineers from a diverse range of backgrounds and personalities. You can handle difficult management situations with tact, empathy and support
• You have significant experience building and deploying ML models using the Python data stack
• Familiarity with modern workflow orchestration tools such as Prefect, Kubeflow, Argo, etc.
• You understand software engineering best practices (version control, unit tests, code reviews, CI/CD) and how they apply to machine learning engineering
• Comfortable spending 30-40% of your time hands-on in the codebase and adapting your technical involvement based on the roadmap

Nice to haves

• Pytorch or Tensorflow deep learning experience
• Experience with Go, C++, Java or another systems language
• Experience using dbt