Head of AI / ML - Energy (Scale-up)

Confidential Opportunity — End Client Disclosed Upon Interview Selection

DISCLOSURE: Most high MsC or PhD from top-tier university in an engineering faculty

Remote / Hybrid possible, team based between San Francisco and Zurich

Attractive compensation, equity, remote flexibility, and real-world impact on the energy systems that power the world.

Background

Have you ever wanted to lead AI initiatives at the frontier of engineering, working on the critical infrastructure that underpins modern civilization? Are you driven by solving high-stakes challenges in renewable energy, power utilities, and oil & gas — across the Middle East, Africa, Europe, and the U.S. — with the freedom of remote work, strong compensation, and equity upside?

We are a boutique consultancy built by PhD engineers, Ex-500 ML leaders, and applied researchers from ABB, Meta, Harvard, and MIT. While Silicon Valley has optimized ad clicks and food deliveries, we’re focused on engineering AI for energy reliability, grid modernization, decarbonization, and industrial resilience.

We’re looking for serious builders — those who can translate industrial complexity into engineered AI solutions and deploy them in environments where reliability isn’t optional.

Role

You’ll lead transformative AI deployments in the energy sector — supporting utilities, renewables operators, and oil & gas majors. You’ll apply state-of-the-art AI to power grid optimization, predictive maintenance, energy forecasting, asset management, and operational safety. This includes neural forecasting for energy loads, GNNs for real-time grid topology, and computer vision for asset monitoring in remote or harsh environments.

Your work will directly influence grid uptime, renewable integration, and the future of energy operations at a global scale.

Academic & Career

• PhD in Computer Science, Energy Systems, Applied Math, or a related field from a top-tier institution
• 10+ years in leadership roles (e.g., VP Data Science, Chief AI Officer) in utilities, renewables, or oil & gas
• Proven experience delivering AI systems in industrial contexts — from predictive maintenance to production optimization
• Board-level credibility and a track record of driving AI adoption in traditional energy organizations

ML & Optimization

• Forecasting & Grid Planning: Transformers and hybrid models for short- and long-term energy forecasting, load balancing, and generation planning
• Graph & Topological AI: GNNs and spatio-temporal models for power grid fault detection, islanding, and distributed energy resources (DER) management
• Reinforcement Learning: RL for energy storage control, grid stabilization, and dynamic routing of energy flows
• Physics-Informed ML: PINNs for simulating turbine dynamics, reservoir behavior, or subsurface flow
• Causal Inference: SCMs and Bayesian networks for root cause analysis of outages and system anomalies

Vision & Sensing

• Computer vision for pipeline inspection, substation monitoring, and solar/wind asset diagnostics (YOLOv8, Mask R-CNN)
• Sensor fusion for remote site monitoring (e.g., vibration, thermal, acoustic data)
• Bonus: Experience with drone/robotics platforms or offshore sensing applications

Deployment & Infrastructure

• Experience with high-throughput data pipelines (Spark, Delta Lake, Ray)
• Model deployment across cloud, edge, and air-gapped/on-prem environments
• Integration with SCADA, EMS, CMMS, historian systems, and industrial IoT platforms
• Expertise in explainability, regulatory compliance, and real-time alerting

Strategy & Domain

• Led AI initiatives in power generation, transmission & distribution, renewables, or upstream/downstream oil & gas
• Strong grounding in energy operations — from maintenance workflows to energy trading and emissions reporting
• Exposure to CO₂ optimization, net-zero transition, and ESG-driven performance
• Global network across utilities, independent power producers (IPPs), and energy majors

Leadership Fit

• Strategic presence in client boardrooms — credible with both engineers and executives
• Willingness to travel for workshops and executive briefings
• Deep conviction in engineering-first AI as a tool for solving the world’s most urgent infrastructure challenges