Machine Learning Engineer at deep tech industrial AI startup

Machine Learning Engineer at deep tech industrial AI startup

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Company

Description:

Deep tech industrial AI startup.

Location: Remote.

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 ope rationalising 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.

• 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.

• 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 product ionising ML models with a focus on CI/CD integration, observability, and system reliability.

Entry level

Full‑time

Engineering and Information Technology

Technology, Information and Internet