MoonPay is a global team imagining and building the products and culture of tomorrow.
We’re driven by our deep belief that blockchain technology will become the status quo, so we commit all our talent to creating the best web3 experiences for everyone, everywhere.
We rely on our open and inclusive culture to foster speed, innovation and responsibilit...
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We’re looking for a seasoned Machine Learning Engineer who understands the full life cycle of training machine learning models and putting them into production. This person can help us build, expand, and maintain our machine learning infrastructure and ecosystem.
In this role, you’ll be a part of the Data team. You’ll help implement machine learning systems that have significant impact on critical functions of the business. In your work, you’ll collaborate closely with MoonPay’s data scientists and operate cross-functionally with Engineering, Ops, Product, Fraud Prevention, and more.
As an ML Engineer, we’ll be looking to you to assess MoonPay’s current ML design, come up with a strategy to effectively expand and improve on ML implementations, and play a key role in ensuring that our ML systems continue to operate effectively.
This role will be hybrid, and will require you to spend some portion of your time in our office in one of these locations.
What You Will Do
Consult with data scientists on training machine learning models (after all, in a production environment, there are implications to model choices that need to be considered)
Provide a strategic vision on how to make machine learning a cornerstone to MoonPay’s business
Support additions and improvements to the ML infrastructure, including getting your hands dirty with data engineering and DevOps engineering
Design systems to meet throughput and latency requirements
Implement NFRs (Non-Functional Requirements) to ensure a high degree of system reliability
Implement and participate in practices (such as an on-call rotation) to ensure the continuous delivery of machine learning services
About You
You’ve been involved in ML before. In the past, you’ve worked closely with data scientists to help them bring experimental features and models to production. You know your way around implementing machine learning systems in a safe and reliable manner, you’re familiar with cloud infrastructure, you have experience with getting complex systems set up in a stable manner,, and you’re aware of potential pitfalls in machine learning systems that should be navigated around. Now, you’re up for a challenge and are interested in having a significant impact on the success of MoonPay. You’re looking for an opportunity to stretch your ML capabilities to help a vibrant business scale up and scale out its machine learning capabilities.
What you will need…
Prior experience with productionising ML systems is a must
Prior experience training machine learning models is highly desirable
Advanced knowledge of Python and familiarity with SQL
Good working knowledge of Terraform and Terragrunt for Infrastructure as Code (IaC)
A solid understanding and hands-on experience with real-time and event-driven systems such as Kafka, Kafkaconnect, Redpanda, Pub/Sub
Solid experience with Kubernetes, docker, deployment types (canary, blue-green etc.)
Experience with setting up CI/CD systems using tools such as CircleCI, drone, Github actions, ArgoCD
Working experience with Big Data technologies such as Spark, Dataflow, and Flink
Experience with system design - keeping performance and efficiency in mind, whilst aware of trade-offs
Experience applying software engineering rigor to ML, including CI/CD/CT, unit-testing, automation etc
Hands-on experience with some MLOps tools such as KubeFlow, DVC, MLFlow
Experience with cloud providers, such as GCP, AWS, or Azure (we are a GCP house)
Prior experience or a strong interest in FinTech, crypto, or web3 preferred
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