Machine Learning Engineer, Modeling
London, England, United Kingdom
Applications have closed
Carbon Re
Carbon Re is a climate tech company using cutting-edge AI solutions to decarbonize cement and steel. Get in touch with us today!Level: Mid-Senior
Hybrid - 2-3 days in London (Southwark)
Salary - £65,000 - £85,000
Who are we?
Carbon Re is an AI research and development company dedicated to reducing gigatonnes of humanity’s carbon emissions each year. We are focused on the energy intensive manufacturing industries; cement, steel, glass, etc. which are responsible for approximately 20% of global emissions and are referred to as ‘hard-to-abate industries’ for which there is currently no viable path to decarbonisation. Our mission is to develop Machine Learning products and solutions that can enable rapid decarbonisation of these industries. Initially this will involve improving and optimising the current manufacturing process but in the long term we aim to rethink and redesign these manufacturing systems for net-zero and accelerate the development of new climate-friendly materials and processes.
We are delighted to have recently raised £4.2m in seed funding led by Planet A Ventures to help us scale up the deployment of our platform, develop new products and continue hiring the best talent in the world.
What are we hiring for?
We are looking for a Software Engineer to join our Machine Learning team, focused on improving the internal models that power our recommendation engines for industrial plants.
You will work with time-series physical and chemical data to build deep learning models. Your goal will be to develop better and more performant models, and to improve our internal understanding of our growing datasets.
Given the innovative and pioneering nature of our work, you will need to be an independent and creative problem solver capable of working under uncertainty on areas of critical importance to the company.
We are also looking for someone with a genuine passion for our mission and a keen interest in researching how we can best optimise these complex industrial processes.
You will primarily work in our shared codebase, using PyTorch as your daily driver. 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 culture of continual improvement.
Requirements
About you:
You will thrive in this role if you:
- Have a strong understanding of Machine Learning, from foundations to modern approaches.
- Have experience (and/or strong interest) in building models that understand and reason about complex physical and chemical processes.
- Have experience with developing machine learning models and recommendation systems for time-series data.
- Enjoy running experiments in a rigorous way.
- Are strongly interested in making an impact towards quickly mitigating climate change.
If you also have experience of Python and at least one of PyTorch, JAX or Tensorflow, this would be very beneficial.
If you don’t have all of the requirements but have a passion for our mission, then please still consider applying. We are building a diverse company and are keen to consider people from a wide range of backgrounds.
Benefits
Our culture:
We don’t draw a specific line between engineering and research teams. We all share the same tech stack, knowledge, tools, and work. We do fundamental machine learning research, and we build and ship commercial-quality software, so with time you will be likely to equally learn and care about many different aspects of our stack. We aim to create software that helps us achieve our mission in a sustainable and principled way, and to share our findings in the open through publications and open-source development.
We are acting now. 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. The cumulative impact of immediate operational improvements on carbon-producing processes will deliver greater reduction in global heating than many longer-term capital intensive solutions. Simply put: we need to prioritise cuts today and our solutions help to achieve this.
We value diversity, equity, and inclusivity. With a diverse range of nationalities and a range of backgrounds represented in our small team, we aim to build 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.
Benefits:
As a young start-up, our benefits package will change and improve quickly as we grow. Our current benefits include:
- A generous EMI stock options scheme
- Pension
- 25 days holiday + 3 Company set grace days (usually falling Christmas/NY) + bank holidays
We are exploring a range of new benefits which we expect to roll out imminently.
Our Interview Process:
- Initial screening call (30-40 minutes)
- Machine Learning Theory chat (45-55 minutes)
- Face to face interview (2 hours) consisting of a pair programming exercise in Python (60 mins) followed by a discussion of a domain-specific research paper (60 mins). This is designed to give you a real world experience of the way we work and the problems we solve.
You will get an opportunity to see the offices, meet some of the team and get a feel for our culture.
If you have any questions about our interview process or require any adaptations then please let us know.
Tags: Deep Learning Engineering Industrial Machine Learning ML models Python PyTorch Research TensorFlow
Perks/benefits: Career development Equity Startup environment
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