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- 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.
- Why this job: Make a real-world impact in climate tech while working with cutting-edge AI tools.
- Qualifications: Experience in ML applications, vector tech, and a startup mindset are essential.
- Other info: Be part of a mission-driven team focused on solving meaningful problems.
The predicted salary is between 36000 - 60000 £ per year.
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.
Founding ML Engineer - Climate Tech employer: Paradigm Talent
Contact Detail:
Paradigm Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Founding ML Engineer - Climate Tech
✨Tip Number 1
Familiarise yourself with the latest advancements in retrieval-augmented generation (RAG) systems and vector databases. Being able to discuss these technologies confidently during your interactions will demonstrate your expertise and passion for the role.
✨Tip Number 2
Engage with the climate tech community by attending relevant meetups or webinars. Networking with like-minded professionals can provide insights into industry challenges and trends, which you can leverage in conversations with us.
✨Tip Number 3
Prepare to showcase your problem-solving skills by thinking of real-world applications for machine learning in climate tech. Be ready to discuss specific examples where you've successfully implemented ML solutions that had a tangible impact.
✨Tip Number 4
Demonstrate your startup mindset by sharing experiences where you've thrived in ambiguous situations. Highlighting your adaptability and willingness to take initiative will resonate well with our mission-driven team.
We think you need these skills to ace Founding ML Engineer - Climate Tech
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly with LLMs and vector databases. Emphasise any projects where you've built ML-driven applications or worked in a startup environment.
Craft a Compelling Cover Letter: In your cover letter, express your passion for climate tech and how your skills align with the company's mission. Mention specific examples of your work that demonstrate your ability to build scalable ML systems and collaborate with users.
Showcase Your Technical Skills: Include a section in your application that details your technical proficiencies, especially in Python, vector databases, and any relevant ML frameworks. Highlight your experience with production-ready systems and any tools mentioned in the job description.
Demonstrate Cultural Fit: Research the company culture and values, then reflect this understanding in your application. Discuss how you can contribute to shaping the team’s direction and culture as a founding member, showcasing your startup mindset and adaptability.
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 building RAG-based systems or developing scalable ML infrastructure. Use examples from your past work to illustrate your thought process.
✨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 specific instances where your input led to improved outcomes or user satisfaction.
✨Align with Their Mission
Research the company’s mission in climate tech and be prepared to articulate why it resonates with you. Show your passion for creating technology that addresses significant challenges and how you can contribute to their goals.
