Lead Machine Learning Engineer & Lead Systems AI Engineer

The ETH Zürich Geothermal Energy & Geofluids (GEG) Group investigates subsurface reactive fluid and geothermal energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and to develop sustainable technologies to address societal needs. Subsurface fluid injection or extraction induces changes in geologic reservoir properties due to thermo-hydro-mechanical-chemical-biological (THMCB) coupled processes. Advancing the understanding of these processes enables the development of subsurface energy extraction and fluid (e.g. CO2 / H2) and energy storage technologies as well as monitoring technologies thereof.

To develop and improve such subsurface technologies and associated monitoring solutions, the GEG group is developing and employing numerical simulators, conducting field analyses, and conducting reactive transport experiments in its reactive transport laboratory. This laboratory currently houses an X-ray Computed Tomography (XRCT) scanner, a laser lab (Particle Image Velocimetry (PIV), Laser-Induced Fluorescence (LIF)), and soon a multi-nuclide MRI scanner.

As Lead Engineer, you will shape the technical foundation of the GEG group’s AI, data, and cloud activities and take end-to-end ownership of complex, multi-year initiatives. We are looking for applicants with a strong background in agentic AI, cloud architecture and software engineering, data engineering and governance, AI and machine learning applications.

• Agentic AI
  — Design, build and operate production-grade AI agent systems, covering the full lifecycle from cognitive architecture through observability, telemetry and rigorous testing and evaluation protocols.
• Cloud architecture & software engineering
  — Conceive, build and operate complex, scalable cloud architectures composed of distributed services and microservices. Drive enterprise-grade software development and take responsibility for the full Software Development Lifecycle (SDLC), from architectural design through deployment and operation.
• Data engineering & governance
  — Design and operate end-to-end data pipelines and data products for structured and unstructured data, including geospatial data where relevant. Establish and maintain solid data governance standards to ensure quality, security and compliance across the data landscape.
• AI & machine learning applications
  — Apply machine learning and AI methods to real-world problems and bring them from prototype into reliable production use.
• Technical leadership
  — Provide technical direction to GEG (and external) scientists and engineers, set architectural standards, mentor team members and orchestrate collaboration across multiple teams and stakeholders.
• Project management & strategic delivery
  — Steer multi-year strategic projects, including roadmap design and budget responsibility; deliver complex, cross-functional initiatives on time and within scope.

You are a lead engineer who combines a strong scientific foundation with substantial hands‑on experience in AI, data, and cloud engineering, and who is ready to take both technical and organizational ownership of complex initiatives. The successful candidate brings:

• A Master's degree or PhD in (Geo-)Physics, Mathematics, Computer Science or a closely related quantitative discipline
• 5+ years of professional experience in software engineering, data engineering or a comparable technical role
• Proven experience in technical and people leadership
  , including guiding engineers, setting architectural direction, establishing engineering standards and mentoring team members
• Solid project management skills, including roadmap design, budget responsibility and the orchestration of cross‑functional collaboration across multiple teams and stakeholders
• Hands‑on experience in the development and operation of AI agents
  , including the surrounding tooling, orchestration, observability, evaluation, and monitoring in production
• Strong practical experience applying machine learning and AI methods to real-world problems, from prototyping through deployment
• Strongly preferred: deep, hands‑on expertise in cloud-native…