Machine Learning Engineer at deep tech industrial AI startup

You will deploy and harden machine learning components in real-world energy and industrial environments. This role bridges the gap between research and production by operationalising physics-informed and deep learning models under real-world constraints. You will ensure the robustness, stability, and explainability of AI systems within complex physical infrastructure and field environments.

Deep tech industrial AI startup

• Own and deploy end-to-end ML pipelines including data validation, feature extraction, model serving, and performance monitoring.
• Adapt foundation models and algorithms to site-specific industrial data and infrastructure constraints.
• Collaborate with research teams to translate novel physics-informed methods into production-grade, reliable systems.

• Proficient in Python and deep learning frameworks like PyTorch or JAX, with experience in Docker and Kubernetes.
• Strong background in applied ML for time series or operational AI within industrial or energy contexts.
• Proven experience productionising ML models with a focus on CI/CD integration, observability, and system reliability.

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• Deploy cutting-edge physics-informed ML models that have a direct impact on global energy and industrial efficiency.
• Work at a well-funded startup backed by top-tier VCs, operating at the intersection of deep learning and physical science.
• Lead the transition from lab-based research to production-grade deployments in complex, high-stakes operational environments.