Staff Engineer, AI/ML
AI in this role
Company Description
We’re Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.
We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.
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.
If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.
With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.
There are a myriad of opportunities to use AI / ML as part of business processes in checkout, and we’re looking for an expert to help us make these a reality. Unlike many such roles, this is an opportunity to truly drive innovation at scale that matters.
We’re looking for a Staff Level AL / ML engineer to accelerate our adoption into the AI era; helping us set our AI vision and show us what is possible.
As part of the Data and AI platform team; you’ll get to pioneer on real world problems, bringing your knowledge of AI / ML, MLOps and LLMs to bear - collaborating cross team to make your vision a reality. You’ll be backed by our platform team, and have a wealth of experience to draw on, but we want someone who’ll blaze a trail; operating on the bleeding edge.
How you’ll make an impact:
Collaborate with teams to research, scope, and validate use cases for AI that drive business value and innovation.
Drive AI adoption by combining rigorous scientific evaluation with the operational maturity to champion high-value applications and confidently push back on unsuitable AI use cases.
Design, refine and build MLOps component of the data and AI platform, from Vector Databases through feature stored and model serving, all at the millisecond scale.
Implement CI/CD pipelines and ensure adherence to best practices for model deployment, security, and compliance with global regulations.
Work as part of our AI / ML guild; having a voice and being a driving force behind new approaches and use cases.
Continuously monitor and optimise system performance to ensure scalability, security, and operational efficiency.
What we’re looking for:
Proficiency in Python (and at least one other language a plus). Experience with key libraries such as PyTorch, Pandas, Hugging Face Transformers, or similar AI toolkits.
Working knowledge of common models, and their use cases and experience applying them to solve specific problems.
Solid engineering skills, including designing and implementing services / data models and features.
Expertise with cloud computing platforms (AWS, GCP, or Azure) and containerisation tools (e.g., Docker, Kubernetes).
Expertise with modern data platforms (e.g., BigQuery / Databricks) and data processing workflows (ETL, pipelines).
Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI)
Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment.
Excellent communication and a drive to work effectively across diverse teams.
We also want to hear if you have:
Experience developing AI / ML applications, including fine-tuning models or creating prototypes.
Awareness of ethical considerations and emerging best practices in AI governance.
Track record of developing rapid prototypes, and bringing them to production with a focus on measurable ROI.
Familiarity with distributed systems and large-scale data processing.
Contributions to open-source projects or a strong GitHub portfolio.
Thought leadership, any articles or talks you’ve given?
High levels of technical curiosity and an eagerness to learn new platforms
Additional information:
Hybrid Working Model: All of our offices globally are onsite 3 times per week (Tuesday, Wednesday, and Friday). We’ve worked towards enabling teams to work collaboratively in the same space, while also being able to partner with colleagues globally. During your days at the office, we offer amazing snacks, breakfast, and lunch options in all of our locations.
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.
Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.
We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.
It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
Life at Checkout.com
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.
For a closer look at daily life at Checkout.com, follow us on LinkedIn and Instagram
How we rate this
Staff Engineer, AI/ML at Checkout.com rates 84 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.
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
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What are the limits of Hugging Face that you've run into, and how did you work around them?
- What's a project where you used Bedrock hands-on?
- Walk me through how you've used PyTorch in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Fine Tuning, ML Ops, Hugging Face, Bedrock, and PyTorch. 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.
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