At a Glance
- Tasks: Build and own ML infrastructure to support climate tech innovations.
- Company: Exciting early-stage climate tech startup with strong backing.
- Benefits: Competitive salary, equity options, and flexible work arrangements.
- Other info: Dynamic role with significant impact and growth opportunities in a collaborative environment.
- Why this job: Join a mission-driven team tackling energy challenges with cutting-edge machine learning.
- Qualifications: Experience in ML Ops or infrastructure within startups; strong Python skills.
The predicted salary is between 80000 - 100000 £ 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.
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.
- 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
Has experience owning ML infrastructure or ML Ops within a startup or scale-up. Understands how to support researchers while building production-ready systems. Enjoys making architectural decisions and building systems from scratch. The stack is flexible—the focus is on finding someone who can make the right long-term engineering decisions rather than someone tied to a specific cloud provider.
Own the ML infrastructure from day one. Equity alongside a competitive salary. London (King's Cross) | 4 days per week in the office (3 may be considered for the right person).
Founding Engineer / MLOps / Data Engineering / Up to £120k in City of London employer: Few&Far
Join a pioneering climate technology startup in King's Cross, London, where you'll play a crucial role as the first ML Ops Engineer in a dynamic and innovative environment. With a strong focus on employee growth, you will have the opportunity to shape the company's ML infrastructure from the ground up while enjoying a competitive salary, equity options, and a flexible work schedule that promotes a healthy work-life balance. This is not just a job; it's a chance to make a meaningful impact in the energy transition sector alongside a passionate founding team.
StudySmarter Expert Advice🤫
We think this is how you could land Founding Engineer / MLOps / Data Engineering / Up to £120k in City of London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Few&Far!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Founding Engineer / MLOps / Data Engineering / Up to £120k at Few&Far.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Few&Far.
✨Apply Directly through Our Website
When you find a suitable opening like Founding Engineer / MLOps / Data Engineering / Up to £120k at Few&Far, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Founding Engineer / MLOps / Data Engineering / Up to £120k in City of London
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.
