Senior Machine Learning Engineer

AI in this role

pytorchtensorflow

ml-ops

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 TeamYou will be joining the Detection team, a team of data scientists and machine learning engineers. The Detection team is responsible for keeping fraud rates low – and clients happy – 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 RoleWe are looking for a Senior Machine Learning Engineer to join our Detection team. In this role, you will be setting the technical direction that bridges data science and engineering. You will be responsible for the architecture, scalability, and reliability of the high-performance ML systems that form the core of our fraud detection platform, with a critical focus on optimizing multi-GPU training for our foundational payments models (e.g., Transformers). Beyond just consuming data, you will take a leading role in defining how data is modeled, stored, and served, directly influencing the architecture of our feature generation pipelines and ensuring data quality throughout the ML lifecycle.

You'll take strategic ownership of our ML infrastructure, championing platforms and tools that empower Data Scientists to rapidly experiment with novel data inputs and model architectures. Your day-to-day will involve close collaboration with engineers and data scientists to operate and optimize machine learning at scale, while also providing mentorship and guidance to other members of the team.

Key Responsibilities

Lead the design, architecture, and orchestration of scalable and reliable end-to-end ML pipelines – from raw data extraction and feature engineering to model training and inference.
Develop high-throughput data pipelines capable of handling terabyte-scale datasets efficiently and optimised for multi-GPU training of foundational transaction models (e.g., Transformers).
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 our ML systems meet overarching business objectives.
Evolve our MLOps infrastructure, driving the strategy for model versioning, automated deployments, monitoring, and observability using modern tools like Prefect.
Mentor and guide other members of the team, fostering a culture of technical excellence and continuous improvement through code reviews, design discussions, and knowledge sharing.
Champion and contribute to the continuous improvement of our internal tools and engineering best practices.

Skills, Knowledge and Expertise

Demonstrable experience designing, building, and deploying complex machine learning systems in a production environment.
Deep understanding of the full machine learning lifecycle, from research to deployment and a track record of leading the design and implementation of scalable training pipelines for large datasets.
Experience with deep learning frameworks like PyTorch or TensorFlow, including distributed/multi-GPU training and transformer architectures.
Familiarity with modern workflow orchestration tools such as Prefect, Kubeflow, Argo, etc.
Working experience leading complex, cross-functional projects and influencing technical direction across multiple teams.
Software engineering fundamentals, including data structures, design patterns, version control (Git), CI/CD, testing, and monitoring.
Exceptional problem-solving skills, with a proven ability to navigate ambiguity and lead technical deep-dives to resolve complex issues.
A collaborative mindset and strong communication skills with the ability to communicate to a range of audiences.

Nice to Haves

Proficiency in a systems programming language (e.g., Go, C++, Java, Rust).
Familiarity with data pipeline tools like dbt.

How we rate this

Senior Machine Learning Engineer at Ravelin rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsPyTorchTensorFlow

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: ML Ops, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

Want an expert to read your CV for this job?

Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.

Get a free CV review

Get new remote machine learning engineer jobs (Builds AI ●●●●) by email

One email a week with the new remote machine learning engineer jobs (Builds AI ●●●●), each rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.

Also alert me for new jobs at Ravelin

Free. One email a week. Unsubscribe in one click.