Autonomous Materials Discovery AI Engineer

CuspAI; Cambridge; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Competitive salary

Equity in CuspAI

28 days holiday (varies by region)

Parental leave

Professional development budget

Interdisciplinary teamwork

Job summary

CuspAI is seeking an experienced Applied AI/ML Engineer (Agents) to design and build the intelligent agents powering our autonomous materials discovery engine. You will craft the agentic framework, connect agents to ML models and simulation engines, and enable scalable task planning and execution.

You will collaborate with chemists and scientists to optimize agent planning, and contribute to end-to-end experimental campaigns that accelerate discovery while learning from data.

Qualifications

• Proficiency in modern ML ecosystem such as PyTorch or JAX, with production experience.
• Strong software engineering skills: scalable systems, testing, CI/CD, and ML ops in production.
• PhD or Masters with 4-5 years industry experience ideal, but candidates with PhD and less industry time considered.

Responsibilities

• Design the agentic framework powering our materials discovery platform.
• Integrate agents with ML models, simulation engines, databases, and compute backends.
• Build pipelines for autonomous planning, scheduling, execution, and interpretation of tasks at scale.

Skills

PyTorch or JAX

Software engineering at scale

LLM-assisted programming

CI/CD & ML Ops

Python

Education

PhD or Masters with 4-5 years industry experience

Tools

Python

CI/CD tooling

ML platforms

Job description

CuspAI is seeking an experienced Applied AI/ML Engineer (Agents) to design and build the intelligent agents powering our autonomous materials discovery engine. You will craft the agentic framework, connect agents to ML models and simulation engines, and enable scalable task planning and execution.

You will collaborate with chemists and scientists to optimize agent planning, and contribute to end-to-end experimental campaigns that accelerate discovery while learning from data.