Machine Learning Engineer
At Carbon Re, we’re on a mission to cut gigatonnes of carbon emissions from the world’s biggest emitting industries (like cement, steel and glass), by applying cutting-edge AI where it matters most.
We’re a small and growing team of scientists, engineers, strategic thinkers and implementers who care deeply about impact and believe in getting there with good humour and a healthy dose of urgency. Our first SaaS product helps cement producers optimise their operations in real time, cutting costs and carbon today while building foundational AI for the next industrial revolution.
With Carbon Re, you’ll do something that actually helps the planet. Not a bad way to invest your career.
About the role
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 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.
What a great fit looks like
You have 1 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 supporting ML development (e.g., TensorFlow, PyTorch).
You have experience working in a scientific environment across disciplines (particularly physics, chemistry, materials science, and engineering), either through previous roles or study.
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 the following: systems dynamics modelling, model predictive control, Supervised Learning, system identification, or Bayesian statistics.
Experience in interfacing with physical systems or unpredictable data sets.
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 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 are not expected to check every box, and we’d love to hear from you even if your experience isn’t an exact match.
We’re building a team that reflects the world we want to change. Our people come from all walks of life - different countries, cultures and experiences - and we think that’s one of our biggest strengths. We’re committed to creating a workplace where everyone feels safe, respected and celebrated. No matter your background, if you’re excited by what we do, we want to hear from you.
In return for your hard work, we’ll give you
💸 A competitive salary (£55k - £85k - Machine Learning Engineer)
📈 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
We’re building a workplace that’s ambitious, kind, and a little bit different. Somewhere you can do your best work alongside people you respect and enjoy being around. Our operating principles help guide how we show up every day:
🏭 Concrete Honesty
Say it how it is. Our honesty is strong, foundational, and built to last.
📈 Forever Optimising
We’re in the business of constantly tweaking, testing and improving.
⏩ Increase Torque
We move with urgency because the climate crisis won’t wait.
🔧 Maintain Reliability
We build trust by showing up, following through, and having each other’s backs.
😄 Cement It with Fun & Kindness
We’re here to extend Earth’s life, but ours is still limited. Let’s enjoy it and be good to each other while we do.
To see these in full, go to Carbon Re’s Operating Principles Notion page.
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.
Intro call - Call with our in-house Talent Partner, Arma
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)
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)
Technical interview - (half day, in person/remote)
Problem solving - applying machine learning, scientific understanding and problem solving to some of the challenges we tackle day to day in the ML team.
Engineering - a practical exercise focused on software engineering for ML.
Architecture - a discussion-based exercise around systems design for ML.
Meet the exec - an informal chat to meet either Josh (CEO) or Buffy (Co-Founder) (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 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.