Carbon Re is on a mission to abate gigatonnes of carbon emissions by applying intelligence to heavy industries.

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.

Carbon Re

Carbon Re is a research and development company with an AI product with the same name, on a mission to remove gigatonnes of CO2 emissions each year by focusing on some of the biggest emitting sectors, such as cement and steel. We leverage world-class expertise from UCL and Cambridge University in AI, energy efficiency, and industrial process improvement.

Our product enables cement producers to optimise their production processes, reducing both fuel costs and emissions. Longer term, we aim to achieve our ambitious mission by leveraging AI and machine learning to design novel industrial processes and speed up the development of new climate-friendly materials.

We recognise the value of bringing different views and perspectives to the table and feel strongly that this is key to developing a successful business. As such, we aim to build a truly diverse company, attracting people from a variety of backgrounds and with different skill sets.

How we operate

Our dream is to create a workplace where every single person is someone you’re genuinely inspired by; someone you respect and learn from. A place filled with lofty ambitions, lots of laughs and intense learning. To achieve this, we embed our operating principles in all that we do, and we expect that new joiners will do the same.

🏭 Concrete Honesty  - Be honest — as concrete forms the foundation of our world, genuine honesty and transparency are our cultural bedrock.

📈Forever Optimising - Always look for ways to improve — we are in the process improvement game, we have high standards and we’re striving to improve.

⏩ Increase Torque - Move with urgency — Our world depends on us, we have a responsibility to find a way to move faster, always.

🔧 Maintain Reliability - Earn trust — Machines break, humans falter. We recognise that to inspire confidence in ourselves, we must earn our teammate’s trust.

Your main responsibilities

Reporting to the Machine Learning Engineering Manager, you will:

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

• Drive technical innovation, using your previous experience to problem solve across the machine learning lifecycle.

• Collaborate with the product team as the technical lead 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 and mentor other team members, improving 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 writing tooling to help 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.

Requirements:

You will be a good fit if:

• You have 5 or more years of working as a machine learning engineer.

• You are familiar with a wide range of ML techniques, having a foundational ML knowledge and experience implementing a range of solutions across multiple problem spaces.

• You are proficient in Python and have a good understanding of the ecosystem of tools and libraries supporting ML development (e.g. TensorFlow, PyTorch etc).

• You have experience of working in a scientific environment across disciplines (particularly physics, chemistry, materials science, engineering), either through previous roles, or study.

• 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.

• 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 working with time series modelling and industrial or IoT data.

• You have experience of systems dynamics modelling, model predictive control, Reinforcement Learning for control systems, system identification 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 to take on the design and development of solutions to difficult problems and drive the progress of new products.

You are not expected to check every box, and we’d love to hear from you even if your experience isn’t an exact match.

Location

• London office or hybrid remote with 2-3 days in our London office each week.

Sponsorships

• Visa sponsorship is not available for this role.

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 Noah, engineering manager; or Bronte, people manager. We’ll have an introductory call with you to make sure that we’re aligned on salary and right to work, and to check any questions on your CV. (15 minutes, remote)

2. 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)

3. 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)

4. Technical interview - two parts (2 hours, 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 discussion around system architecture, design and best practices for machine learning

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 offer you the opportunity to reference-check us by chatting informally with any team members you didn’t meet as part of 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 on any stage.

Benefits

• Competitive salary (£85k - £100k)

• Equity in the form of share options

• Flexible working arrangements

• 30 PTO days

• Generous pension scheme

This is an ideal opportunity to be early into a growing software company that is applying cutting-edge AI technologies to make a meaningful impact on carbon emissions and climate change.

More about Carbon Re

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 fundamental ML research to commercial-grade software development, offering diverse learning and impact opportunities.

Immediate action for long-term impact. Due to the cumulative radiative forcing effect, one tonne of carbon saved today will help us meet global temperature targets as much as two tonnes saved in 2050. Immediate carbon reduction has a more profound effect on global temperature goals than future efforts. Our solutions prioritize immediate operational improvements for significant climate impact. We understand the urgency of now.

We value diversity, equity, and inclusivity. With a diverse range of nationalities and a range of backgrounds represented in our small team, we are building an inclusive environment where our people can bring their authentic selves to work, be respected for who they are and the exceptional work they do. We welcome and actively encourage applications from all sections of society and are committed to offering equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, marital, domestic or civil partnership status, sexual orientation, gender identity, parental status, disability, age, citizenship, or any other basis. We see our diversity as an asset as we tackle challenging problems through technology.