At a Glance
- Tasks: Build and optimise ML systems for real-time dispute processing.
- Company: Join a tech powerhouse trusted by eBay, Spotify, and Uber.
- Benefits: Flexible hybrid work model, competitive salary, and impactful projects.
- Other info: Collaborative culture that empowers you to take ownership from day one.
- Why this job: Make a difference with cutting-edge ML technology in a dynamic environment.
- Qualifications: 5+ years in MLOps/ML Engineering with strong Python skills.
The predicted salary is between 63000 - 77000 £ per year.
You might not know our name, but companies like e Bay, Spotify, Klarna, Uber, and Sony do, because we're behind many of the digital experiences you use every day.
Whether you want to book a holiday, order food, renew a subscription, or check out online, there's a good chance our tech powers the payments behind the scenes.
Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.
As an ML (Machine Learning) Engineer at Checkout. com in the Disputes ML team, you will contribute to the development of our brand-new, ML-driven dispute optimisation suite.
You will be building solutions that power our stack of value added services in the Disputes area.
We're a growing team in an expanding area within the company and we're looking for individuals who have strong ownership, are passionate about productionising ML and have a pragmatic approach to converting big problems into smaller iterations to constantly deliver value.
How you'll make an impact Build systems for training, deploying and monitoring machine learning models used in our Disputes platform, at scale Build and optimize data pipelines and backend services to process dispute and payment data in real time Build and scale our feature store for use-cases both online and offline Take complete ownership of delivering comprehensive, end-to-end features within a startup-like setting, driving the entire lifecycle from requirement refinement, data pipeline construction and model training to troubleshooting and production deployment Turn raw data into production-ready features that feed our dispute systems Collaborate with platform and backend engineers to integrate models seamlessly Experience and qualifications5+ years of experience as MLOps /ML Engineer High proficiency in writing clear, production-ready Python code Experience with production ML models (online or offline) and standard MLOps practices Experience with monitoring and observability of production systems, with a strong sense of ownership Experience with training and operating models on Databricks Familiarity in Cloud-based application development (we use AWS & Azure)Familiarity with one or more ML frameworks and technologies: scikit-learn, xgboost, Tensor Flow, Py Torch, Spark, Sage Maker, Vertex AI, Kubeflow, Seldon, Triton Strong communication skills, able to express ideas clearly and collaborate across teams Additional Information Bring all of you to work We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.
Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Senior MLOps and Machine Learning Engineer in London employer: Checkout.com
At Checkout.com, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. With our London headquarters, we offer a dynamic work environment where employees are empowered to take ownership of their roles and contribute to meaningful projects from day one. Our commitment to employee growth is evident through clear career advancement opportunities and a supportive hybrid working model that promotes work-life balance, making us a top choice for talent acquisition professionals seeking to make a significant impact in the fintech industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior MLOps and Machine Learning Engineer in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Checkout.com or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Checkout.com.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Checkout.com.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Checkout.com that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Senior MLOps and Machine Learning Engineer in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Checkout.com.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Checkout.com and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Checkout.com
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Checkout.com uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
