At a Glance
- Tasks: Build and own ML infrastructure to support cutting-edge climate tech solutions.
- Company: Exciting early-stage climate tech startup in King's Cross, London.
- Benefits: Competitive salary up to £120k, equity, and flexible 4-day work week.
- Other info: Fast-paced environment with significant ownership and growth opportunities.
- Why this job: Join a founding team and make a real impact on the energy transition.
- Qualifications: Experience in ML Ops, cloud infrastructure, and large-scale geospatial datasets.
The predicted salary is between 90000 - 120000 £ per year.
We're partnering with a well-funded, early-stage climate technology startup that's using cutting-edge machine learning to tackle one of the biggest challenges facing the energy transition. Backed by leading industry investors and already working with enterprise customers, they're now looking for their first ML Ops Engineer to build the infrastructure that will power the next stage of the company's growth.
The Role
This is a true founding infrastructure role. You'll take ownership of everything that sits between machine learning research and production - building the platforms, pipelines and engineering practices that allow the team to move quickly and scale with confidence. Working alongside the CTO and ML Engineer, you'll own the ML infrastructure from the ground up, making key architectural decisions from day one.
You'll be responsible for:
- Owning and evolving the company's ML infrastructure and compute environment
- Building and automating scalable data pipelines
- Improving developer workflows and ML engineering practices across the team
- Structuring codebases and deployment workflows to support future growth
- Making key cloud infrastructure decisions (Azure today, AWS is an option)
- Helping bridge the gap between research and production systems
We're looking for someone who...
- Has experience owning ML infrastructure or ML Ops within a startup or scale-up
- Understands how to support researchers while building production-ready systems
- Has worked with large-scale geospatial datasets (this is a key requirement)
- Enjoys making architectural decisions and building systems from scratch
- Wants to join a genuinely early-stage company where they'll have significant ownership
- Thrives in fast-moving environments with lots of autonomy
Tech
- Azure (with the opportunity to evaluate AWS)
- Python
- ML pipelines
- Cloud infrastructure
- CI/CD
Founding Engineer / MLOps / Data Engineering / Up to £120k employer: Few&Far
Join a pioneering climate technology startup in King's Cross, London, where you'll play a crucial role as a Founding ML Ops Engineer. With a strong focus on innovation and sustainability, the company offers a collaborative work culture that encourages autonomy and creativity, alongside competitive compensation and equity options. This is an exceptional opportunity to shape the future of ML infrastructure while contributing to meaningful environmental change.
StudySmarter Expert Advice🤫
We think this is how you could land Founding Engineer / MLOps / Data Engineering / Up to £120k
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We think you need these skills to ace Founding Engineer / MLOps / Data Engineering / Up to £120k
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Few&Far, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Few&Far. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Few&Far
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Few&Far!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
