At a Glance
- Tasks: Join our team to build and deploy innovative machine learning models for climate impact.
- Company: Gigaton, a mission-driven tech company focused on reducing carbon emissions.
- Benefits: Equity options, flexible working hours, 30 days holiday, and a generous pension scheme.
- Other info: Collaborative environment with opportunities for growth and innovation.
- Why this job: Make a real difference in combating climate change while advancing your ML skills.
- Qualifications: 2+ years in machine learning, proficient in Python, and passionate about sustainability.
The predicted salary is between 50000 - 70000 £ per year.
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 applying cutting‑edge AI where it matters most. We’re a small and growing team of scientists, engineers, and strategic thinkers who care deeply about impact and believe in getting there with good humour and urgency. Our SaaS products help heavy industry optimise operations in real time, cutting costs and carbon today while building the foundation for the next industrial revolution.
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:
- 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.
- 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.
Benefits:
- 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.
Machine Learning Engineer employer: Gigaton
At Gigaton, we pride ourselves on being an exceptional employer dedicated to making a meaningful impact on climate change through innovative technology. Our collaborative work culture fosters creativity and growth, offering employees the chance to engage in diverse projects while enjoying flexible working arrangements and generous benefits, including equity options and a robust pension scheme. Join us in our mission to revolutionise heavy industry and contribute to a sustainable future, all while being part of a supportive and dynamic team.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at Gigaton. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it’s GitHub repos or a personal website, let your work speak for itself. We love seeing what you can do!
✨Tip Number 3
Prepare for the interview by brushing up on your ML knowledge and practical applications. Be ready to discuss your past projects and how they relate to Gigaton’s mission. We want to see your passion for making an impact!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for candidates who are genuinely interested in our mission to tackle climate change.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight relevant experience, especially in ML techniques and tools like Python, scikit-learn, and PyTorch. We want to see how your skills align with our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for climate change mitigation and how you can contribute to our goals. Keep it engaging and let us know why you're excited about joining our team at Gigaton.
Showcase Your Projects:If you've worked on any interesting ML projects, make sure to mention them! Whether it's time-series modelling or reinforcement learning, we love seeing practical applications of your skills. Include links to your GitHub or portfolio if you have one!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen to be part of our journey from the get-go!
How to prepare for a job interview at Gigaton
✨Know Your ML Stuff
Make sure you brush up on your machine learning techniques and tools, especially Python and libraries like scikit-learn and PyTorch. Be ready to discuss specific projects where you've applied these skills, as this will show your practical experience and understanding of the concepts.
✨Show Your Passion for Impact
Gigaton is all about cutting carbon emissions, so express your enthusiasm for climate change mitigation. Share any relevant experiences or projects that highlight your commitment to making a positive impact in this area, as it aligns with their mission.
✨Prepare for Collaboration Questions
Since the role involves working closely with product teams and other engineers, think about examples from your past where you've successfully collaborated on projects. Highlight your ability to communicate effectively and contribute to a cohesive team environment.
✨Understand Their Tech Stack
Familiarise yourself with the technologies and methodologies used at Gigaton. If you have experience with cloud platforms like AWS, GCP, or Azure, be prepared to discuss how you've leveraged these in your previous roles, as this will demonstrate your readiness to hit the ground running.