At a Glance
- Tasks: Design and implement ML systems for climate tech, collaborating with users and owning projects end-to-end.
- Company: Join an early-stage tech company making unstructured documents useful for informed decision-making.
- Benefits: Enjoy remote work options, high ownership, and a chance to shape the company's culture.
- Other info: Be part of a mission-driven team influencing product direction from the ground up.
- Why this job: Make a real-world impact in climate tech while working with cutting-edge AI tools.
- Qualifications: Experience in building ML applications, especially with LLMs and vector databases.
The predicted salary is between 36000 - 60000 € per year.
Founding Machine Learning Engineer – Climate tech
London preferred, remote considered (UK-based)
Early-stage | High ownership | Real-world impact
About the Company
We’re an early-stage technology, our platform makes unstructured documents searchable, explainable, and useful - giving users the insights they need to make faster and more informed decisions. We’re building a founding team of engineers who are passionate about solving meaningful real-world problems in the climate-tech space using cutting-edge AI. We’re already working with forward-thinking organisations and are now focused on scaling the next version of the product.
The Opportunity
We’re looking for a Founding ML Engineer to join our small, mission-driven team. This is a rare chance to build systems from the ground up, define the product alongside users, and work with modern ML tooling - not just in theory, but in production.
What You’ll Do
- Build RAG-Based Systems: Design and implement production-ready retrieval-augmented generation (RAG) pipelines powered by LLMs and vector search.
- Develop ML Infrastructure: Create scalable, modular ML systems for parsing and interpreting long-form unstructured documents.
- Collaborate Closely with Users: Work directly with internal and external stakeholders to refine use cases and deliver high-utility outputs.
- End-to-End Ownership: Operate across the stack—from model experimentation to integration with APIs and UIs.
- Influence Culture & Direction: Help shape technical standards, processes, and company culture as a key early team member.
Who You Are
- Applied ML Engineer: Experience building ML-driven applications using LLMs, with an emphasis on real-world deployment rather than just experimentation.
- Strong with Vector Tech: You’ve worked with vector databases and know how to structure embeddings and retrieval for scalable performance.
- Startup-Minded: You’re comfortable wearing multiple hats and navigating ambiguity with a bias toward action.
- Curious & Fast-Learning: You pick up new tools quickly, iterate fast, and are thoughtful about technical tradeoffs.
- Mission-Aligned: You care about creating tech that solves important problems in a responsible and lasting way.
What We’re Working With
We’re early-stage and flexible, but current tools include:
- Python (and typical ML ecosystem)
- Vector databases
- Open-source and API-based LLMs
- Retrieval frameworks (e.g., LangChain, LlamaIndex)
- Cloud-native infra (AWS)
We care more about your ability to learn and execute than ticking every tech box.
Why Join
- Shape the Future: Join at the ground floor and influence everything from the product roadmap to team values.
- Mission-Driven Work: Build tools that help organisations make better decisions in complex domains.
- Work Autonomously: High trust, high impact environment with real ownership from day one.
- Growth-Oriented: Work closely with experienced founders and early users to rapidly develop your skills and career.
StudySmarter Expert Advice🤫
We think this is how you could land Founding ML Engineer - Climate Tech in London
✨Tip Number 1
Familiarise yourself with the latest advancements in retrieval-augmented generation (RAG) systems and large language models (LLMs). Being able to discuss recent developments or case studies during your conversations can demonstrate your passion and expertise in the field.
✨Tip Number 2
Network with professionals in the climate tech and machine learning sectors. Attend relevant meetups, webinars, or conferences to connect with like-minded individuals and potential future colleagues. This can help you gain insights into the industry and may even lead to referrals.
✨Tip Number 3
Showcase your ability to work in a startup environment by highlighting any previous experiences where you wore multiple hats or navigated ambiguity. Be prepared to share specific examples of how you adapted quickly to changing circumstances or took initiative in past projects.
✨Tip Number 4
Research the company’s mission and values thoroughly. Be ready to articulate how your personal values align with theirs, especially regarding creating technology that addresses real-world problems. This alignment can set you apart as a candidate who is genuinely invested in their mission.
We think you need these skills to ace Founding ML Engineer - Climate Tech in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights relevant experience in machine learning and climate tech. Focus on projects where you've built ML-driven applications, especially those involving LLMs and vector databases.
Craft a Compelling Cover Letter:Write a cover letter that showcases your passion for climate tech and your understanding of the company's mission. Mention specific examples of how your skills align with the role and how you can contribute to building impactful solutions.
Showcase Your Projects:Include links to any relevant projects or GitHub repositories that demonstrate your experience with ML systems, particularly those that involve retrieval-augmented generation or working with unstructured documents.
Prepare for Technical Questions:Be ready to discuss your technical expertise in ML, vector technologies, and cloud infrastructure during interviews. Prepare to explain your thought process in previous projects and how you approached problem-solving in ambiguous situations.
How to prepare for a job interview at Paradigm Talent
✨Showcase Your ML Experience
Be prepared to discuss your previous projects involving machine learning, especially those that focus on real-world applications. Highlight any experience you have with LLMs and vector databases, as this will be crucial for the role.
✨Demonstrate Problem-Solving Skills
Expect to face scenario-based questions where you'll need to explain how you would approach specific challenges in climate tech. Use examples from your past work to illustrate your thought process and problem-solving abilities.
✨Emphasise Collaboration
Since the role involves working closely with users and stakeholders, be ready to discuss how you've successfully collaborated in previous roles. Share examples of how you gathered feedback and refined use cases based on user input.
✨Express Your Passion for Climate Tech
Make sure to convey your enthusiasm for climate technology and its impact. Discuss why you are motivated to work in this field and how you see your skills contributing to meaningful solutions.
