AI Engineer

Contract: Full-time, permanent. Location: Hybrid (London office). Level: 2-4 years professional experience. Salary: £75K+ depending on experience.

About Materiom: Materiom is an innovation platform for regenerative materials R&D. Our mission is to accelerate the development and adoption of bio-based materials that can replace petrochemical plastics, and have a net-positive impact on the planet. We're a small, interdisciplinary team of around twelve people spanning materials science, AI/ML, software engineering, circular economy, and design. In 2026, we're at an inflection point: moving from an open, philanthropically-funded platform toward a commercial product, while keeping our public-good mission intact. Our core bet is that combining curated experimental data, domain expertise, and AI-based modelling can dramatically cut the time and cost of bio-based materials R&D.

The role: We're looking for a mid-level AI Engineer to work at the intersection of applied ML and applied AI; someone equally comfortable developing predictive models on structured scientific data as they are with building LLM-powered tools and agentic workflows. You'll work within our tech team to contribute across two primary workstreams:

• Predictive modelling: developing and iterating on ML models that map bio-based formulation design spaces, and building the MLOps infrastructure to support active learning.
• LLMs and agentic systems: developing and evaluating internal & external tooling that integrate Materiom's data and model intelligence into AI agents and frontier AI product platforms.

What you'll do:

• Design, build, and deploy ML models for predicting properties of bio-based formulations from structured experimental and literature-mined data.
• Develop, operate and maintain pipelines for data mining, model training, evaluation, and active learning workflows against lab equipment/partners.
• Build and improve LLM-powered tools and agentic systems.
• Deploy ML/AI-driven tooling to project partners, pilot users and beyond in order to gather user feedback.
• Run rigorous experiments to compare modelling approaches, interpret results clearly, and iterate toward quality goals.
• Contribute to LLMOps and MLOps practices (versioning, monitoring, evaluation, cost/quality optimisation).
• Effectively communicate complex technical concepts and findings to multidisciplinary audiences.
• Work closely within the tech team and stay closely attuned to product and scientific priorities, translating them into well-scoped technical work.

What we're looking for. You'll need:

• Master's degree in a technical field (e.g., Computer Science, Artificial Intelligence, Machine Learning).
• Around 2 to 4 years of professional experience in a data-driven or ML engineering environment.
• Solid grounding in ML fundamentals.
• Hands-on experience with structured/tabular data and real-world model development and evaluation.
• Practical experience building with LLMs: prompt engineering, RAG, tool use, agentic frameworks and evaluation.
• Strong Python skills and good software engineering habits: version control, testing, reproducible pipelines.
• Familiarity with a major cloud platform (e.g. GCP).
• Excellent problem-solving and analytical skills, and the ability to work with high agency and autonomy in an ambiguous environment.

Useful but not required: active learning or closed-loop experimentation workflows; familiarity with scientific data from chemistry, materials science, or adjacent domains; experience with relevant open-source frameworks; background in or curiosity about bio-based materials or sustainability.

Benefits: Competitive salary with performance bonuses, 30 days paid time off annually, learning and mentorship grants, flexible hybrid working (London-based), international company retreats, Bike2Work scheme.