AI/ML Engineer - Materials Discovery (Hybrid London)

Materiom; Greater London; Hybrid; GBP 70,000 - 100,000; Full time

Benefits offered by this job

Competitive salary

Annual bonus

30 days paid holiday

Mentorship grants

Commuter support

Flexible hybrid working

International retreats

Collaborative culture

Job summary

Materiom is seeking a mid-level AI/ML Engineer to apply ML/AI to materials discovery and support a semi-automated lab. You will work across predictive modelling, data sampling, and agentic systems within a small, interdisciplinary tech team.

The role focuses on designing models, building agentic systems, and contributing to MLOps, with a strong emphasis on transparency and collaboration in a startup setting.

Qualifications

• Master’s degree in a technical field and 2–4 years of professional experience in data-driven or ML engineering environments.
• Solid grounding in the modern ML stack: model training/evaluation, experience with ML tooling and ML lifecycle.
• Excellent Python programming and reproducible pipelines with version control and testing.

Responsibilities

• Design, build, and deploy structure property prediction ML models from experimental and literature-mined data.
• Develop and instrument internal and external agentic systems with traceability and versioning.
• Create rigorous evaluation pipelines and experiments to compare modelling approaches against benchmarks.
• Contribute to MLOps and Software Engineering best practices (CI/CD, monitoring, cost/quality).
• Deploy ML/AI tooling to project partners and gather user feedback.

Skills

Python

PyTorch

Scikit-learn

MLFlow

Agentic AI

Bayesian optimization

Active learning

Education

Master's degree in CS/AI/ML/related

Tools

LangGraph

PydanticAI

ADK

GCP

CI/CD

Job description

Materiom is seeking a mid-level AI/ML Engineer to apply ML/AI to materials discovery and support a semi-automated lab. You will work across predictive modelling, data sampling, and agentic systems within a small, interdisciplinary tech team.

The role focuses on designing models, building agentic systems, and contributing to MLOps, with a strong emphasis on transparency and collaboration in a startup setting.