Senior Machine Learning Engineer

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 Senior Machine Learning Engineer to help build the models that underpin these control systems and help us level up our machine learning infrastructure.

We don’t draw a specific line between engineering and research 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

Reporting to a Machine Learning Team Lead, you will:

• Work in the machine learning team as an individual contributor, building, testing and deploying our models.

• Contribute to technical innovation and problem-solving across the machine learning lifecycle.

• Collaborate with the product team on customer projects, planning, designing and delivering the work packages required, as well as playing a significant role in the development of our wider 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 several 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 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 prior experience with time-series modelling and industrial or IoT data.

• You have experience in any of: dynamical systems, 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 or chemistry.

• 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.

The interview process

We run a multiple-part interview process. You can choose to interview remotely or on-site for some of the interviews, but it’s easier to build rapport in person.

1. Intro call - Meeting with our talent partner

2. Fundamentals of Machine Learning - A discussion with members of the machine learning team around some of the fundamentals of ML and your understanding and application of them. (1 hour, remote)

3. Technical interview - (half day, in person/remote)

   1. Problem solving - applying machine learning, scientific understanding and problem solving to some of the challenges we tackle day to day in the ML team.

   2. Engineering - a practical exercise focused on software engineering for ML.

   3. Architecture - a discussion-based exercise around systems design for ML.

4. Behaviours and Operating Principles- A meeting with two members of our team to discuss your past experiences, to understand how you would fit in with our operating principles. (1 hour, remote)

5. Meet the exec - an informal chat to meet either Josh (CEO) or Buffy (COO) (30 minutes, in person/remote)

In the same way we reference-check our candidates before making final offers, we invite you to reference-check us by chatting informally with any team members you didn’t meet during the hiring process.

Once the interviews are over, we’ll try to make a decision as quickly as possible, and you can ask us for feedback at any stage.

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.