Senior Machine Learning Engineer

London – Hybrid – Competitive salary + equity

About Deep Medical

Deep Medical helps healthcare systems treat more patients by recovering capacity that would otherwise be lost. We are helping drive down the NHS waitlist. Our AI saves lives. Every day, appointments go unused while patients wait for care. Capacity disappears through cancellations, reschedules and non-attendance, creating a gap between the resources healthcare organisations have and the care they are able to deliver. Our mission is to solve that problem through Healthcare Capacity Intelligence - a new category of AI-powered infrastructure that helps healthcare organisations identify, recover and redeploy capacity before it is lost. Our platform continuously analyses operational data and predicts opportunities to recover capacity before it disappears. The result is shorter waiting times, improved utilisation of clinical resources and better outcomes for patients. We’re already delivering meaningful results across the NHS, and the next phase is scaling the platform, deepening the intelligence and expanding into new healthcare markets.

The role

We’re looking for a Senior ML Engineer to help architect, build and scale the machine learning systems at the core of Deep Medical’s products, from research and experimentation through to production and scale. We build our own ML models in-house. You’ll work across ML R&D, model productionisation, distributed systems, healthcare data infrastructure and AI-powered services, partnering closely with CS, product and engineering teams to bring new capabilities into production. This isn’t a role about maintaining legacy models or incrementally improving internal tooling. You’ll help build the intelligence and the technical foundations of a category-defining healthcare AI company.

What you’ll do

• Research, prototype and evaluate machine learning approaches, and translate the most promising into production.
• Own the full ML lifecycle: training pipelines, model serving, monitoring, retraining and the MLOps tooling that keeps models reliable in production.
• Design and build the scalable backend services and APIs that put models in front of real users.
• Architect systems capable of processing large volumes of sensitive healthcare data securely and reliably.
• Own model and system reliability, performance and operational excellence through monitoring, alerting and observability.
• Design secure systems that meet the demands of healthcare and regulated environments.
• Maintain our AWS Quicksight data screens and automated client reporting.
• Influence technical and ML strategy and contribute to architectural decision-making.
• Mentor engineers and help raise the quality and effectiveness of engineering across the organisation.
• Work closely with product and operational teams to deliver solutions with measurable impact.

What we’re looking for

• 5+ years in software development in industry.
• Bachelor’s, Master’s or PhD degree in Computer Science, Machine Learning, Statistics, or a related quantitative field; equivalent practical experience is also valued.
• Strong foundation in Computer Science and Algorithms, including data structures, algorithm design, and software engineering best practices.
• Strong ML R&D ability - reading, evaluating and implementing recent research.
• Proven experience productionising ML models, not just prototyping or research.
• Strong Data Science skills.
• Strong backend engineering skills in Python and SQL.
• Experience building and maintaining APIs (REST, GraphQL and/or gRPC).
• Strong ability with databases, data modelling and system design.
• Strong knowledge and experience with AWS.
• Demonstrated architecture and system-design ability, taking technical ownership of systems from design through to production.

• Knowledge of and experience with reinforcement learning.
• Experience with Docker, ECS, Kubernetes or similar container platforms.
• Experience with infrastructure-as-code such as Terraform.
• Familiarity with UK GDPR, information security and compliance frameworks.
• Experience working with large, complex datasets, ideally in healthcare or another regulated domain, and an understanding of privacy and security considerations.

What success looks like in your first 12 months

You’re a trusted technical owner of our core ML systems and platform. You’ve taken new AI capabilities from research through to production. You’ve improved model performance, platform scalability, reliability and observability in measurable ways.

What we offer

Competitive salary plus meaningful equity. Hybrid working from our London office. Direct access to founders, customers and clinical problems genuinely worth solving.