Machine Learning Engineer

My client is looking for a Machine Learning Engineer with strong fundamentals in classical machine learning and experience (or keen interest) in generative AI and multimodal large language models.

The role:

Build and deploy production ML systems from initial research through to live deployment, managing the complete life cycle of computer vision models in the field. Design scalable data infrastructure to handle large ecological datasets, implementing pipelines for ingestion, processing and model serving across cloud and edge environments. Lead hardware integration for field monitoring devices, adapting models for deployment on single-board computers whilst maintaining accuracy under real-world constraints. Drive continuous improvement of model performance through monitoring, experimentation and staying current with computer vision and ecology AI research. Develop robust APIs that connect our ML systems to client applications, ensuring reliable access to species identification and biodiversity metrics.

3+ years of building and shipping software as a Software Engineer, Machine Learning Engineer or similar engineering or technical role. Computer vision in production where you've deployed CV models that real users depend on, whether from industry production deployments, research implementations or substantial side projects that demonstrate your capability. Cloud infrastructure experience building data pipelines, managing storage and databases, and orchestrating ML workflows on AWS, Azure or GCP. Production ML deployment experience including containerising models, managing inference services and troubleshooting systems under load. Experience working with large datasets and the practical engineering challenges of storage, processing and pipeline optimisation at scale. Comfortable with early-stage uncertainty where priorities evolve quickly, roles aren't rigidly defined, and you'll solve problems outside your typical scope. You have a bias for action you thrive in ambiguity with an ability to move quickly while maintaining high standards. Engaged with ML research keeping up with relevant papers and new techniques, testing novel methodologies in both classical and generative AI.

Experience with hardware integration whether professionally or through side projects involving single-board computers like Arduino, Raspberry Pi or ESP32. Active side projects in ML, hardware.