Machine Learning Engineer

Gigaton; Greater London; On-site; GBP 70,000 - 120,000; Full time

Benefits offered by this job

Equity in the company

Flexible working

30 days holiday + bank holidays

Pension scheme

Job summary

Gigaton in the United Kingdom is seeking a Machine Learning Engineer (mid to principal) to build models that underpin autonomous control systems for industrial processes and help reduce emissions.

You will work in a cross-disciplinary ML team, deploying end-to-end solutions and contributing to scalable infrastructure, with a focus on real-world impact and climate goals.

Qualifications

• 2+ years of ML engineering experience.

Responsibilities

• Build and deploy ML models and controllers.

Skills

ML experience

Python

End-to-end ML

Climate impact

Cloud infra

Education

Degree in ML/CS

Tools

scikit-learn

PyTorch

AWS

GCP

Azure

Job description

At Gigaton, we're on a mission to cut gigatonnes of carbon emissions from the world's biggest emitting industries (like cement, steel and glass), by building autonomous AI control and optimisation systems that learn and leverage the physics of manufacturing. Our products run heavy industrial plants more efficiently, more stably, and with lower emissions in real time - laying the foundation for the next industrial revolution.

We are a team of scientists, engineers, builders, and operators who love hard problems, have high standards, and want to make change happen in the physical world. We care about deep tech, but we care even more about whether it delivers cost and carbon impact in a live plant, with real people, under real constraints.

With Gigaton, you'll solve really tough problems in places few people ever get close to, and build something that actually helps the planet. Are you up for the challenge?

We are seeking a Machine Learning Engineer (ranging from mid to principal) to help build the models that underpin these control systems and help us level up our machine learning infrastructure.

Whether in research or deployment focussed teams, we operate as one cohesive unit, sharing tech stack, knowledge, and objectives. Our focus spans from fundamental ML research to commercial-grade software development, offering diverse learning and impact opportunities.

Your main responsibilities

• Build state-of-the art systems which combine edge control with active learning algorithms to continuously deliver impact and reduce CO2 emissions.
• Work in the machine learning team as an individual contributor, building, testing and deploying our models, optimisers and controllers.
• Contribute to technical innovation and problem-solving across the machine learning lifecycle.
• Collaborate with cross-functional teams on customer projects, planning, designing and delivering the work packages required, as well as playing a significant role in the development of our product.
• Help establish best practices to improve our internal processes.
• Contribute to the design and implementation of robust, maintainable and scalable machine learning systems.

You will also contribute to our fear-free development process by building tooling that helps the team move faster and more sustainably. You will be supported by continuous builds, tests, a constructive review system, and a strong culture of improving engineering processes.

What a great fit looks like

• You have 2 or more years of experience as a machine learning engineer.
• You are familiar with ML techniques, and have both theoretical ML knowledge and experience implementing different types of solutions.
• You are proficient in Python and have a good understanding of the ecosystem of tools and libraries that support ML development (e.g., scikit-learn, PyTorch).
• You have experience working in end-to-end machine learning deployments.
• You have experience working in a scientific environment across disciplines (particularly physics, chemistry, materials science, and engineering), either through previous roles or study.
• Are passionate about making a positive impact on climate change mitigation and possess a strong interest in our mission.

You'll excel if

• You have experience in any of: control systems, time-series modelling, reinforcement learning, system identification, optimisation or bayesian statistics.
• You are used to working in a fast-paced startup environment with an agile process.
• You have a degree in machine learning, physics, chemistry, engineering, mathematics or computer science.
• You have a track record of developing novel ML methods in relevant fields.
• You are hungry for responsibility, enthusiastic about taking on the design and development of solutions to difficult problems, and eager to drive the progress of new products.
• You have a solid understanding of modern cloud compute infrastructure as it relates to machine learning, and experience in working with AWS, GCP, Azure, or other vendors.
• You have have designed and built systems end to end with software architectural best practice.

In return for your hard work, we'll give you

• Equity in the company: When we win, you win. You'll get share options, so you're part of our journey from the inside.
• Flexible working We trust you to know how and when you work best and to work that out with your team.
• 30 days of holiday (plus bank holidays). Rest is productive. Take the time you need to recharge
• A generous pension scheme. We're planning for the future in more ways than one.

Our Operating Principles

• Go Gig or Go Home: High Bar, All In. What we do matters to humanity, to our customers and to each other. We hold ourselves to an extraordinarily high bar and bring the urgency this mission requires.
• Concrete Honesty: Be honest. As concrete forms the foundation of our world, genuine honesty and transparency are the bedrock of our culture.
• Autonomous Ownership: High agency, high ownership. We build systems that take control and make things better. We do the same: see it, own it, drive it.
• Cement it with Kindness & Fun: Have fun, be kind. We're here to extend Earth's life, but ours is still limited. We want to enjoy the ride. To see these in full, go to Gigaton's Operating Principles Notion page.