AI/ML Engineer - Materials Discovery

Materiom; Greater London; Hybrid; GBP 75,000 - 95,000; Full time

Benefits offered by this job

Competitive salary

Bonuses

30 days holiday

Mentorship grants

Bike2Work

Flexible hybrid

International retreats

Collaborative culture

Job summary

Materiom is seeking a mid-level AI/ML Engineer to join our hybrid London team, applying AI/ML to materials discovery at the intersection of biology, chemistry, and data science. You'll work on predictive modelling, agentic systems, and data infrastructure within our lab-driven tech stack.

You’ll collaborate with engineering and scientific teams to design experiments, build models, and deploy ML tooling for partners and pilots, while embracing agility in a startup setting and translating

Qualifications

• Master’s degree in a technical field plus 2–4 years in data-driven or ML engineering work.
• Strong Python skills and solid ML stack experience.
• Hands-on with model training, evaluation, and modern agentic frameworks.

Responsibilities

• Design, build, and deploy predictive ML models for bio-based formulations and related datasets.
• Develop agentic systems with traceability, performance tracking, and versioning.
• Create evaluation pipelines and experiments to compare approaches and improve performance.
• Contribute to MLOps and software engineering best practices (CI/CD, monitoring, cost/quality).
• Deploy ML tooling to partners and pilots and gather user feedback.

Skills

Python

PyTorch

Scikit-learn

MLFlow

Agentic frameworks

CI/CD

Education

Master’s degree in a technical field

Tools

GCP

Job description

AI/ML Engineer - Materials Discovery

Contract: Full-time, permanent
Location: Hybrid (London office)
Level: 2-4 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 an autonomous 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 mid-level AI Engineer to work at the intersection of applied ML,applied AI, and our semi-automated lab; someone excited about the potential of AI for scientific discovery, comfortable with agentic AI, predictive modelling, and keen to work on applying AI for science.

You’ll work within our tech team to contribute across three 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.
• 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: 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 & agentic systems

• Design, build, and deploy structure property prediction ML models of bio-based formulations from experimental, in-silico and literature-mined data.
• Develop & instrument internal and external agentic systems with rigorous traceability, performance tracking, and versioning.
• Develop rigorous evaluation pipelines and experiments to compare modelling approaches, interpret results clearly, and iterate against internal performance benchmarks.
• Contribute to MLOps and Software Engineering best practices, including but not limited to versioning, monitoring, evaluation, cost/quality optimisation, CI/CD, Agentic Skills, 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 productionize efficient data sampling techniques (Bayesian optimization, active learning) to design our experimental and in-silico data generation campaigns.
• Develop, productionize and maintain data infrastructure, sourcing & storing data from the open-access literature, simulation pipelines, and our semi-automated lab.
• Interface with Materiom’s autonomous lab to debug and improve robotic workflows and data streaming in collaboration with our Research Scientist.

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:

• Genuine excitement about the potential of transforming the materials sector through AI-enabled materials discovery of breakthrough bio-based alternatives.
• Master’s degree in a technical field (e.g., Computer Science, Artificial Intelligence, Machine Learning, Chemistry, Materials Science, Chemical Engineering, Mechanical Engineering), plus 2 to 4 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.), to modern agentic frameworks (PydanticAI, ADK, LangGraph, etc.); you have hands‑on experience with what’s happening inside your models, as well as modern LLM agentic frameworks.
• 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.

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.
• Background in materials science, formulation development, or genomics, and familiarity with handling scientific data from bioinformatics, chemistry, or related domains.
• 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 £75K+ 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