ML Engineer for Autonomous Industrial AI & Control (Equity)

Gigaton; Greater London; Hybrid; GBP 75,000 - 120,000; Full time

Benefits offered by this job

Equity in the company

Flexible working

30 days holiday plus bank holidays

Generous pension scheme

Job summary

Gigaton is developing autonomous AI control and optimisation systems to cut emissions in heavy industry. We seek a Machine Learning Engineer (mid to principal) to build models underpinning our control systems and advance our ML infrastructure.

You will contribute across the ML lifecycle in a mission-driven, fast-paced startup, collaborating with researchers, engineers and operators to deliver real-world impact.

Qualifications

• 2+ years of experience in machine learning engineering.
• Theoretical ML knowledge and experience implementing different solutions.
• Proficient in Python and ML libraries (scikit-learn, PyTorch).
• Experience in end-to-end ML deployments.
• Experience in scientific environments (physics, chemistry, materials science, engineering).
• Passion for climate change mitigation and mission alignment.

Responsibilities

• Build state-of-the-art systems combining edge control with active learning.
• Work as an individual contributor building, testing and deploying models, optimisers and controllers.
• Contribute to ML lifecycle innovation and problem-solving.
• Collaborate with cross-functional teams on customer projects and product development.
• Help establish best practices to improve internal processes.
• Design and implement robust, scalable ML systems.

Skills

2+ years ML engineer

Python

End-to-end ML deployment

Climate tech interest

Cloud knowledge (AWS/GCP/Azure)

Education

Bachelor's degree in ML/physics/chemistry/engineering/maths/CS

Tools

scikit-learn

PyTorch

Cloud platforms

Job description

Gigaton is developing autonomous AI control and optimisation systems to cut emissions in heavy industry. We seek a Machine Learning Engineer (mid to principal) to build models underpinning our control systems and advance our ML infrastructure.

You will contribute across the ML lifecycle in a mission-driven, fast-paced startup, collaborating with researchers, engineers and operators to deliver real-world impact.