Senior Machine Learning Engineer | Python | PyTorch | Machine Learning | Large Language Models | RAG | Remote, UK and EU

As a Senior ML Engineer, you’ll be the technical leader driving machine learning infrastructure from experimentation to production, ensuring AI-powered solutions deliver measurable impact for customers worldwide. This is a unique opportunity to join as one of the early engineering team members of a well-funded startup building breakthrough applications of large language models (LLMs) and AI agents.

You’ll take full ownership of evaluation frameworks, production ML pipelines, and cross-team ML integration, working closely with company leadership and product teams to transform cutting-edge AI research into robust, scalable solutions. Your success will be measured by agent performance improvements and product innovation impact, not just technical metrics. This role is ideal for a hands-on ML engineer who has scaled production ML systems, thinks like a product builder, and wants to drive the productionization of LLMs and ML to solve real-world problems.

Your Contributions:

• Build Production-Grade Evaluation Systems: Design and implement evaluation frameworks that measure performance, track improvements, and ensure consistent value delivery.
• Drive Experimentation-to-Production Pipeline: Own the ML lifecycle from prototype to production, enabling rapid iteration while maintaining reliability.
• Enable Cross-Team ML Integration: Collaborate with product teams to integrate ML into customer-facing features.
• Optimize AI Agent Performance: Improve systems through experimentation, prompt engineering, and architecture enhancements.
• Scale ML Infrastructure: Develop foundational systems, monitoring, and tooling to support rapid growth.
• Partner with Leadership: Work closely with senior leadership while operating with high autonomy.
• Mentor Through Excellence: Provide guidance and mentorship to junior ML engineers.

• Production ML Experience: 5+ years building and scaling ML systems in production.
• Neural Networks Foundation: Strong background in classical and deep learning before specializing in LLMs and transformers.
• Product-Focused Mindset: Track record of integrating ML systems into real products.
• Multi-Company Perspective: Experience across startups and/or scale-ups.
• Technical Versatility: Strong Python skills and adaptability across frameworks and tools (e.g., LangChain, workflow orchestration).
• Self-Directed Leadership: Ability to operate autonomously while aligned with leadership.
• Cross-Functional Collaboration: Experience translating technical capabilities into business value.

Nice to Haves:

• Experience with AI agents, LLMs, or generative AI applications
• Domain knowledge in cybersecurity or related fields
• Background at ML-first companies
• Experience with modern MLOps and cloud ML infrastructure
• Track record of optimizing model performance and costs