Senior Machine Learning Engineer

An Introduction to Primer

Primer is the unified infrastructure for global payments. We give finance and payments teams the visibility and control to reduce complexity, improve performance, and capture more revenue - all from a single platform.
Backed by Sofina, Peak XV Partners, ICONIQ, Tencent, Accel, and Balderton, we're building the payments layer the world's best companies rely on.

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Read up on our $100m Series C

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You’ll be our first Senior Engineer in ML, working alongside our Staff Engineer to help build the foundations from the ground up. There’s no existing DS/ML team yet, so the two of you will shape it together, working closely with engineering, data, and product, who are already exploring how ML can be used in payments. There’s a lot still to figure out and a lot to build.

You’ll work closely with your team’s stakeholders to identify where ML can move the needle, and report to engineering leadership while that function takes shape.

• Own the full lifecycle of ML-powered features within your initiatives - from independently researching and testing approaches through training, deployment and monitoring in production

• Product ionise smart routing decisions across the payment flow, building the ML infrastructure around them as you go - versioning, CI/CD, observability

• Partner closely with product, data and engineering to turn promising use cases into shipped, measured outcomes

• Bring a pragmatic, impact-first mindset to experimentation and model evaluation, rather than chasing the most sophisticated approach

• Mentor other engineers as they pick up ML techniques, sharing how you think about tradeoffs and when to keep things simple

• Talk directly to customers to validate ideas and pressure-test what you’re building against real usage

• Help set technical direction within your area, working with your team to prioritise the use cases worth pursuing

• Establish patterns and practices that make ML work reliable and repeatable at Primer, building on foundations rather than defining them alone

• Senior experience in ML engineering, data science, or applied research, with real production deployments behind you (API, batch, or streaming)

• You think statistically. You can design and run experiments and A/B tests, and report what the results do and don’t show

• Strong Python skills and hands‑on experience with ML libraries such as scikit-learn, XGBoost, Tensor Flow, PyTorch or Keras

• Solid grounding in modern software engineering, infrastructure and data tooling, and an understanding of the MLOps challenges across the full ML lifecycle. You don’t need to have built a platform, but you know what one has to handle

• Familiarity with reinforcement learning (multi‑armed or contextual bandits, for example). Production experience with it isn’t required, but you need to understand how it works and where it applies

• Cloud experience — AWS preferred; GCP or Azure both fine

• Exposure to payments or e-commerce is a plus, not a prerequisite - you’ll build that domain depth on the job

• Comfortable with ambiguity, in the problems and in the process. You can research, develop and product ionise independently, and you’ll help define how we work as you go

• You enjoy working in an office setting — we’re remote‑first, and always will be

• You need a fully mapped‑out roadmap before you start — we’re building this function, and there is a lot yet to be defined.

• You prefer handing models to another team to deploy and run.

• An initial intro call with a Talent Partner

• An interview with the Hiring Manager

• Challenge Stage - Contextualised to the role

• A final, values‑alignment interview