AI​/ML Engineer

Company Description

We’re  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.

• 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.

• 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., Big Query / 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.

• 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 Git Hub portfolio.
• Thought leadership, any articles or talks you’ve given?
• High levels of technical curiosity and an eagerness to learn new platforms.

• Hybrid Working Model:
  All of our offices globally are onsite 3 times per week (Tuesday, Wednesday, and Friday). We’ve…