Senior ML Engineer: Build Production ML for Payments (Remote)

Primer; United Kingdom; À distance; GBP 90 000 - 140 000; Plein temps

Avantages offerts par ce poste

Fully remote globally

Competitive equity options

25 days holiday minimum

Home office stipend £500

Private Medical Insurance

Learning budget

Résumé du poste

Primer is the unified infrastructure for global payments, trusted by leading merchants and backed by top investors. The Senior ML Engineer will shape Primer's ML foundations, working with engineering, data, and product to deploy impactful, production-ready models that improve payments routing and outcomes.

You will own ML feature lifecycles, drive experiments, and build scalable MLOps practices while mentoring peers and collaborating with stakeholders across the company.

Qualifications

• Must have real production ML deployments (API, batch, or streaming).
• Experience designing and running experiments and A/B tests.
• Strong software engineering foundation and data tooling awareness.

Responsabilités

• Own full lifecycle of ML-powered features from research to deployment and monitoring.
• Productionise ML decisions within the payment flow with robust CI/CD and observability.
• Collaborate with product, data, and engineering to ship measurable ML outcomes.
• Mentor engineers on ML techniques and tradeoffs; keep solutions simple where possible.
• Engage with customers to validate ideas and test hypotheses in real usage.
• Help set technical direction and prioritise high-impact use cases.

Connaissances

Python

ML frameworks

MLOps

AWS

Reinforcement learning

Experimentation

Outils

scikit-learn

XGBoost

TensorFlow

PyTorch

Keras

Description du poste

Primer is the unified infrastructure for global payments, trusted by leading merchants and backed by top investors. The Senior ML Engineer will shape Primer's ML foundations, working with engineering, data, and product to deploy impactful, production-ready models that improve payments routing and outcomes.

You will own ML feature lifecycles, drive experiments, and build scalable MLOps practices while mentoring peers and collaborating with stakeholders across the company.