AI engineer in materials discovery

HireAlice; Greater London; Hybrid; GBP 63,000 - 77,000; Full time

Benefits offered by this job

30 days paid holiday

Skills development budget

Bike2Work scheme

Flexible office/remote work

Company retreats

Job summary

Materiom in London is seeking a skilled AI/ML engineer to design, build, and deploy structure-property prediction models for bio-based formulations. You will work with experimental, in-silico, and literature data, collaborating with scientists to define features and evaluation criteria.

You will implement end-to-end pipelines, contribute to LLM-based agents, and help maintain MLOps practices, cloud deployments, and reproducible experiments, while communicating complex concepts to

Qualifications

• Genuine excitement about AI-enabled materials discovery in bio-based systems.
• Strong ML fundamentals, validation, and uncertainty estimation.
• Experience with end-to-end ML-powered systems and model deployment.
• Proficiency with PyTorch, scikit-learn, MLFlow, and cloud platforms.

Responsibilities

• Design, build, and deploy structure-property prediction ML models.
• Quantify model confidence and guide experiments or simulations.
• Collaborate with scientists on features, data standards, and targets.
• Develop, operate, and maintain data extraction, training, and orchestration workflows.

Skills

ML fundamentals

Python

PyTorch

scikit-learn

LLM-based agents

Data curation

Cloud platforms

Education

Master’s degree in Computer Science/AI

Tools

PydanticAI

CrewAI

Google ADK

Job description

Описание:

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.

Задачи:

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. Run rigorous experiments to compare modelling approaches, interpret results clearly, and iterate toward quality goals. Quantify how confident models are in their predictions, and use that to decide which experiments or simulations to run next. Work with materials scientists to define new features and target properties, data standards and success criteria for each model. Contribute to Materiom’s agentic stack for scientific discovery, ranging from hypothesis generation to closing the loop with Materiom’s SDL and simulation engine. Build tool interfaces, such as MCP servers, that give scientists and agents structured access to internal and external resources such as Materiom’s databases, predictive models, simulation engine and lab instruments. Track developments in AI for science and agentic research, and prototype promising methods against Materiom’s use cases. Develop, operate and maintain workflows for data extraction, model training & evaluation, and orchestration workflows against lab equipment/partners. Contribute to LLMOps and MLOps best practices, including versioning, monitoring, evaluation, cost/quality optimisation. Deploy ML/AI-driven tooling to project partners, pilot users and beyond in order to gather user feedback. 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.

Требования:

Genuine excitement about the potential of transforming the materials sector through AI-enabled materials discovery of breakthrough bio-based alternatives. Solid grounding in ML fundamentals: statistical learning, neural networks, optimisation, model selection and validation, uncertainty estimation, and working with small, noisy or imbalanced datasets. Practical experience developing ML-powered systems end to end, from data curation, collection and preparation to model choice and hyperparameter tuning, to model deployment. You have experience with PyTorch, scikit-learn, MLFlow, etc. Practical experience building LLM-based agents, improving them through metrics-driven evaluation, ideally including research loops where agents propose, run and evaluate experiments to optimise agent performance or to develop ML models. You have experience with e.g. PydanticAI, CrewAI, Google ADK etc. Familiarity with at least one advanced deep learning technique 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, or physics-informed neural networks (PINNs). Master’s degree in a technical field (e.g., Computer Science, Artificial Intelligence, Machine Learning), plus 2 to 4 years of professional experience in an AI start up environment. Strong Python skills and good software engineering habits: version control, testing, reproducible pipelines. Familiarity with a major cloud platform (e.g. GCP, AWS). Experience working in an environment that translates well into a small startup setting. You have high agency & autonomy in an ambiguous environment. You thrive at shipping progress without needing everything defined upfront. Будет плюсом: 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

Условия:

• An annual salary range of £70K+, commensurate with your experience and expertise.
• Eligibility for performance-based bonuses to reward your contributions to the company’s success.
• 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.
• An annual individual budget dedicated to developing your hard and soft skills.
• Access to a Bike2Work scheme to support sustainable travel.
• A highly flexible scheme that includes weekly days at the office (London) and at home, as well as options for temporary remote work.
• Regular company retreats, often held in international locations, to build connection and celebrate progress.
• 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.