Founding ML Engineer - Climate Tech

JR United Kingdom; Stevenage; Remote; GBP 50,000 - 80,000; Full time

Job summary

A leading technology company is seeking a Founding ML Engineer to shape the future of their innovative platform. This role involves building and implementing cutting-edge ML systems while collaborating closely with users to solve real-world problems in climate tech. You will have the opportunity to influence the product and company culture from the ground up.

Benefits

High trust and autonomy

Opportunity for rapid skills development

Influence team values and product roadmap

Qualifications

• Experience building ML-driven applications using LLMs.
• Strong experience with vector databases and structuring embeddings.
• Ability to handle multiple roles in a startup environment.

Responsibilities

• Design and implement production-ready RAG pipelines powered by LLMs.
• Create scalable ML systems for parsing unstructured documents.
• Work with stakeholders to refine use cases.

Skills

Machine Learning

Vector Databases

Python

Cloud-native Infrastructure

Retrieval-Augmented Generation

Job description

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Client:

Paradigm Talent

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Other

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EU work permit required:

Yes

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Job Views:

2

Posted:

06.06.2025

Expiry Date:

21.07.2025

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Job Description:

? 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)
• 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.