ABOUT YOU

We’re looking for a driven and collaborative technical leader who is passionate about using ML and AI to make a tangible impact on helping organisations understand and manage their impact on nature. You should thrive in a dynamic, purpose-driven environment and be eager to tackle challenging, real-world problems.

You will bring:

• Significant experience in ML engineering or applied data science.
• A proven track record of successfully designing, building and deploying ML models into production environments at scale.
• Expertise in Python and relevant ML libraries.
• Strong experience with various ML algorithms (e.g., supervised, unsupervised, reinforcement learning) and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Experience developing generative AI applications and deploying them into production.
• Experience working with cloud platforms, ideally Google Cloud Platform (GCP) and BigQuery.
• Proficiency in working with and querying global scale datasets.
• Experience working with bioinformatics, life sciences, or geospatial data.
• Strong understanding of software engineering best practices, including version control (e.g. Git), CI/CD, and testing.
• Strong understanding of MLOps principles and monitoring and evaluation tools.
• Experience of providing technical mentorship and guidance to junior ML engineers and data scientists.
• Excellent communication and stakeholder management skills, with the ability to distil complex ideas for diverse audiences.
• A collaborative mindset and a desire to work cross-functionally across a purpose-driven business.

Desirable but not required:

• Experience in a technical lead role.
• Experience working with software as a service (SaaS) products.

ABOUT US

Backed by ambitious investors, we’re on a mission to make biodiversity measurable at scale to underpin global goals and accelerate finance into nature. NatureMetrics combines tech and AI to derive simple insights from the full complexity of nature on the ground.
We’re a dynamic team passionate about nature and biodiversity and we take immense pride in our work and our impact. Join us on this journey!

THE ROLE

NatureMetrics is a global leader in biodiversity monitoring and environmental DNA (eDNA) analysis, transforming the scale at which nature can be quantified. Our cutting-edge solutions enable organisations to monitor nature impact across sectors, from conservation to industry and inform sustainability decisions with unprecedented accuracy. With a strong market leading position, NatureMetrics has established a robust client base and developed a proprietary software platform that makes biodiversity insights accessible, actionable and scalable. As Earthshot Prize Finalists 2024, BloombergNEF Pioneers 2024, World Economic Forum Technology Pioneers 2024 and TechNation Future Fifty 2025 cohort members, we have the potential and the opportunity to change the way organisations operate.
As we continue to grow, we’re looking for a highly skilled and experienced Lead Machine Learning (ML) and artificial intelligence (AI) Engineer to drive the development and deployment of our advanced ML and AI learning solutions. In this pivotal role, you’ll combine hands-on technical leadership with strategic vision, to build innovative models and scalable infrastructure that tackle complex ecological and geospatial challenges. You’ll be instrumental in shaping our ML and AI roadmap, directly contributing to helping organisations understand and manage their impact on nature.

KEY RESPONSIBILITIES

Hands-on technical leadership: Lead the design, development and deployment of end-to-end ML and AI systems, from research and prototyping to productionisation and monitoring.
Model development: Lead the development and implementation of advanced ML algorithms and models to solve complex ecological and geospatial problems.
System design and architecture: Architect, build and maintain scalable, reliable, and efficient ML and AI pipelines and infrastructure.
Mentorship: Provide technical guidance, project management,and mentorship to a team of ML engineers and data scientists, fostering their growth and ensuring high-quality deliverables.
Product strategy and innovation: Collaborate with product managers and stakeholders to translate business requirements into technical solutions, contributing to the overall ML and AI strategy and identifying opportunities for innovation.