Staff Machine Learning Engineer

Overview

As a Staff ML/AI Engineer at Accurx, you will set the technical direction for AI/ML capabilities across the organisation and ensure scalable, maintainable production systems. You will own end-to-end quality for ML, addressing privacy, bias, and security while avoiding single-expert dependencies. Your role spans mentoring engineers, building reusable platform capabilities, and driving AI/ML opportunities that align with clinical and product goals. You’ll work closely with cross-functional teams to translate healthcare needs into impactful AI products. This is a high-impact, mission-driven opportunity to advance healthcare communication at scale.

Pay / Benefits

• office in Shoreditch (hybrid)
• flexible working with core hours 10am-4pm
• 28 days holiday + bank holidays
• enhanced parental leave and fertility support
• free breakfasts, snacks and lunches
• equity opportunities

Responsibilities

• Define and steer the technical direction for AI/ML across multiple teams and align with the product roadmap
• Own end-to-end ML quality strategy, including privacy, bias, security, and maintainability
• Act as a force multiplier by building reusable platform capabilities and mentoring engineers into senior roles
• Identify and champion AI/ML-powered product opportunities, translating clinical objectives into scalable capabilities
• Influence roadmaps beyond your immediate team and guide build-vs-buy decisions when appropriate
• Collaborate across engineering, product, data, and security to ensure robust, scalable solutions

Key requirements

• Deep expertise across ML techniques (Transformer NLP, Deep Learning, Tree-based, Bayesian methods)
• Proven track record shipping models from experiments to production with scalable pipelines and monitoring
• Ability to set technical direction and influence product roadmaps and architectural decisions
• Mastery of production-grade languages (Python, C#, or Go) and system design for extensibility
• Strong mentoring and communication skills to grow engineers and communicate with stakeholders

• Mentorship
• Strategic communication
• Cross-functional collaboration
• Transformer-based NLP
• Deep Learning
• Tree-based methods

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