Senior AI/ML Engineer - Materials Discovery

Senior AI/ML Engineer - Materials Discovery

Contract: Full-time, permanent

Location: Hybrid (London office)

Level : 3-5 years professional experience

About Materiom

Materiom's mission is to create a world where materials regenerate nature and support human health. We pursue it by building data and AI tools that help discover natural alternatives for packaging, textiles, and the built environment. We support innovators around the world to bring these new materials to market.

We're an interdisciplinary team bringing together materials engineering, computational materials science, AI/ML engineering, and business innovation. In 2026, we're at an inflection point: with advanced AI and a self-driving lab, we are accelerating the discovery of natural materials and supporting innovators across the sector.

The role

This is a high-impact role with the potential to apply leading AI/ML to one of the world's greatest challenges. We're looking for a Senior AI Engineer to work at the intersection of applied ML, applied AI, and our self-driving lab; someone excited about the potential of AI for scientific discovery, with deep expertise in ML model development for predictive modelling and representation learning, keen to work on applying AI for science.

You'll work within our technical team to contribute across three primary workstreams

• Predictive modelling & representation learning : developing and iterating on ML models that learn the high-dimensional, multi-objective, natural polymers design space.
• Efficient data sampling techniques: developing and consolidating Bayesian optimization and active learning scientific workflows for experimental & in-silico data generation campaigns.
• Material discovery agentic systems : collaborate with our technical team in developing and evaluating internal & external tooling that integrate Materiom’s data and model intelligence into AI agents for scientific discovery.

What you'll do

ML modeling

• Design, build, and deploy structure property prediction ML models of bio-based formulations from experimental, in-silico and literature-mined data. In pursuit of a base model that generalizes across materials systems.
• Develop rigorous evaluation pipelines and experiments to compare modelling approaches, interpret results clearly, and iterate against internal performance benchmarks.
• Contribute to internal and external agentic systems with rigorous traceability, performance tracking, and versioning.
• Contribute to MLOps and Software Engineering best practices, including but not limited to versioning, monitoring, evaluation, cost/quality optimisation, CI/CD, etc.
• Deploy ML/AI-driven tooling to project partners, pilot users and beyond in order to gather user feedback.

Experimental design & data infrastructure

• Work closely with our engineering and scientific team to design, benchmark, simulate and deploy efficient data sampling techniques in the lab (Bayesian optimization, active learning) to design our experimental and in-silico data generation campaigns.
• Interface with Materiom’s self-driving lab to incrementally close the loop between experimental runs and agentic systems.

Internal & external-facing collaboration

• 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

• Experience applying AI/ML techniques within materials science, formulation development, or genomics , and familiarity with handling scientific data from bioinformatics, chemistry, or related domains.
• Experience with advanced deep learning techniques such as geometric learning (equivariant GNNs), spatio-temporal techniques (SSMs, neural ODEs), world models (JEPA, latent dynamics models), language modeling (chemical and protein LMs), diffusion models (score-based models, flow matching), or physics-informed neural networks (PINNs).
• Genuine excitement about the potential of transforming the materials sector through AI-enabled materials discovery of breakthrough bio-based alternatives .
• Master’s degree and/or PhD in a technical field (e.g., Computer Science, Artificial Intelligence, Machine Learning, Chemistry, Materials Science, Chemical Engineering, Mechanical Engineering), plus 3 to 5 years of professional experience in a data-driven or ML engineering environment.
• Solid grounding in the modern ML stack: from model training & evaluation (PyTorch, scikit-learn, MLFlow, etc.)
• Strong Python skills and good software engineering habits: modularity, reusability, testing, version control, reproducible pipelines, CI/CD, etc.
• 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.
• Experience and confidence in technical leadership and strategic decision-making

Useful but not required

• Experience with agentic systems for scientific discovery; autoresearch, AI Co-Scientists, etc.
• Experience with active learning or closed-loop experimentation workflows.
• An interest in robotics orchestration & automation applied to scientific domains.
• PhD in a related discipline.

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 £85K-£95K full-time equivalent, 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