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Machine Learning Ops Engineer
hace 3 meses
Hi, we're MoonPay. We're here to onboard the world to Web3.
Why? Because we think Web3 is a unique and democratizing technology. It gives people back control of their money, digital identity, data, and property like nothing else before it.
What we do
We're the leading infrastructure company in Web3. This means we offer our partners everything from payment solutions (we call them 'Ramps') to minting software for digital collectibles, like NFTs. And over 16 million people around the world now trust our products — just take a look on Trustpilot.
We're also big on collaborations. And we've worked on stunts, drops, and partnerships with some of the world's most prestigious and forward-thinking brands.
But that's not all. At the start of 2023, we launched our own consumer app because we wanted to see if we could build a better Web3 account. It's taken off in a big way, so we're working to hone it further.
So whatever your background, we're sure there's something for you here. Come help us build the future of Web3 and digital ownership.
Locations Supported
Lisbon, Madrid, Barcelona, Krakow, Bucharest, Cape Town
About The Opportunity
We're looking for a seasoned Machine Learning Ops (MLOps) Engineer who 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 MLOps 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.
What you will do...
Provide a strategic vision on how to make machine learning a cornerstone to MoonPay's businessWork collaboratively with MoonPay's data scientists to productionize machine learning features and modelsSupport additions and improvements to the ML infrastructure, including getting your hands dirty with data engineering and DevOps engineeringDesign systems to meet throughput and latency requirementsImplement NFRs (Non-Functional Requirements) to ensure a high degree of system reliabilityImplement 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 MLOps 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 MLOps capabilities to help a vibrant business scale up and scale out its machine learning capabilities.
Most importantly, though, you will embody the core principles that everyone here at the MoonPay lives by. Our "BLOCK Values" are at the heart of everything we do - and they are...
B - Be Humble
L - Lead with Empathy
O - Own It
C - Communicate with Clarity
K - Kaizen
What you will need...
Prior experience with productionising ML systems is a must.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.
Research has shown that women are less likely than men to apply for this role if they do not have experience in 100% of these areas. Please know that this list is indicative, and that we would still love to hear from you even if you feel that you are only a 75% match. Skills can be learnt, diversity cannot.
Perks
Equity package
Unlimited holidays
Paid parental leave
Annual training budget
Home office setup allowance
Monthly budget to spend on our products
Working in a disruptive and fast-growing industry where the possibilities are endless
Freedom, autonomy and responsibility
Commitment To Diversity
At MoonPay we believe that every voice matters. We strive to create a mindful and respectful environment where everyone can bring their authentic self to work, and experience a culture that is free of harassment, racism, and discrimination. That's why we are committed to diversity and inclusion in the workplace and are a proud equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status or any other characteristic protected by law. This policy applies to all employment practices within our organization, including, but not limited to, hiring, recruiting, promotion, termination, layoff, and leave of absence.
MoonPay is also committed to providing reasonable accommodations in our job application procedures for qualified individuals with disabilities. Please inform our Talent Team if you need any assistance completing any forms or to otherwise participate in the application process.
