The role

NatureMetricsis a global leader in biodiversitymonitoringand environmental DNA (eDNA) analysis, transforming the scale at which nature can be quantified. With a strong market leading position,NatureMetricshasestablisheda robust client base and developed a proprietary software platform that makes biodiversity insights accessible,actionableand scalable. As we continue to grow,we''relooking for a highly motivated and skilled Machine Learning (ML) and Artificial Intelligence (AI) Engineer to contribute to the development and deployment of our advanced ML and AI solutions. In this hands-on role,you''llbe involved in building innovative models and scalable infrastructure that tackle complex ecological and geospatial challenges.You''llplay a key part in our ML and AI roadmap, directly contributing to helping organisations understand and manage their impact on nature.Lead on the design, development and deployment of end-to-end ML and AI features, from research and prototyping to productionisation and monitoring.Model implementation:

Implement advanced ML algorithms and models to solve complex ecological and geospatial problems.Contribute to the architecture, building and maintenance of scalable, reliable, and efficient ML and AI pipelines and infrastructure.Collaborate with product managers and stakeholders to translate business requirements into technical solutions, supporting the overall ML and AI strategy and identifying opportunities for innovation.AtNatureMetricsdiversity and inclusion are part of our DNA. Itfuels our innovation and connects us to the communities we work with. Were looking for a driven and collaborative engineer who is passionate about using ML and AI to make a tangible impact on helping organisations understand and manage their impact on nature. 2+ years of experience in ML engineering or applied data science.- A proventrack recordof successfully contributing to the design, building and deployment of ML models into production environments.- Expertisein Python and relevant ML libraries- Experience with various ML algorithms (e.g. supervised, unsupervised, reinforcement learning) and deep learning frameworks (e.g. Experience developing generative AI applications and deploying them into production.- Familiarity working with bioinformatics, life sciences or geospatial data.- Understanding of software engineering best practices, including version control (e.g. Git), CI/CD, and testing.- Understanding ofMLOpsprinciples and monitoring and evaluation tools.- Strong prior experience working with bioinformatics, life sciences or geospatial data in an ecological context.Experience working with software as a service (SaaS)products.Competitive SalaryFlexible work arrangementsOpportunities for professional growthBenefits package including salary sacrifice pension scheme prioritising sustainability; life assurance;Enhanced annual leave;Cycle to Work Scheme; enhanced family friendly policy.#