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 work is genuinely novel. If that sounds like the kind of problem you want to spend your time on, read on.

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 — you understand what's actually happening inside your models, not just how to call the API.
• 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.
• The ability to work with high agency & autonomy in an ambiguous environment. You thrive at shipping progress without needing everything defined upfront.
• Experience working in an environment that translates well into a small startup setting.

Useful but not required:

• Proficiency in getting ML/AI-centric artefacts in front of users for obtaining real-world feedback.
• Experience with 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 genuine curiosity about bio-based materials or sustainability.

What We Offer

Materiom is an impact-focused startup offering a supportive and flexible environment where you can drive the acceleration of net-positive materials using cutting-edge technology. Our benefits include

• Competitive Salary: An annual salary range of £75K+, commensurate with your experience and expertise.
• Annual Bonuses: Eligibility for performance-based bonuses to reward your contributions to the company’s success.
• Generous Paid Time Off: 30 days of paid holiday per year for full-time positions (adjusted pro-rata for part-time positions), in addition to all UK bank holidays.
• Learning & Mentorship Grants: An annual individual budget dedicated to developing your hard and soft skills.
• Commuter Support: Access to a Bike2Work scheme to support sustainable travel.
• Flexible Hybrid Working: A highly flexible scheme that includes weekly days at the office (London) and at home, as well as options for temporary remote work.
• International Retreats: Regular company retreats, often held in international locations, to build connection and celebrate progress.
• Collaborative team culture: Our culture is defined by deep, interdisciplinary collaboration, offering you the exciting opportunity to work at the intersection of materials science and AI to drive positive impact for people and the planet