Principal AI/ML Lead

Company Description Drill Insight is a premier provider of AI-powered technology services and digital subsurface solutions headquartered in Perth, Australia, with active operations and offices across the North Sea, UK, Middle East, Asia Pacific, and Australia — including strategic hubs in Dubai, Kuala Lumpur, and India.

At Drill Insight, we deliver Data-Driven Insights – Powered by AI. We specialize in developing hybrid machine learning (ML) models, generating subsurface intelligence, enhancing reservoir characterisation, optimizing field development and drilling, and ultimately maximizing production outcomes for our global clients.

Having successfully executed over US $22.6 million in contracts, our impact is driven by an elite team of 16 multidisciplinary experts spanning data science, artificial intelligence, geoscience, well engineering, reservoir engineering, geology, geophysics, and project management. Our strength lies in integrating traditional oilfield expertise with cutting-edge ML/AI and digital technologies, enabling unparalleled optimization across subsurface, drilling, and production domains.

Our AI/ML-driven Field-Insights, Well-Insights, and Drill-Insights platforms support Oil and Gas E&P companies’ planning, operational and digital objectives. Our integrated team of drilling engineers, geoscientists, and data scientists has delivered AI/ML solutions for Chevron, Woodside, Equinor, Aker BP, SNOC and others - helping operators forecast drilling hazards, extract insight from offset wells, refine drilling targets, trajectories, well placements, and improve well performance, deliverability across varied basin conditions. Our integrated solutions Field-Insights, Well-Insights, and Drill-Insights can deliver measurable value through Field Development Planning, Cloud-Based Well Review Automation, and Real-Time Drilling Visualisation, Performance Evaluation, Benchmarking and Optimisation Platform.

We are looking for builders rather than title collectors—people who can work across disciplines, challenge assumptions, communicate clearly with drilling engineers and turn difficult operational problems into reliable production systems.

Oil and gas experience is valuable, but exceptional candidates from industrial AI, robotics, mining, autonomous systems, process control, aerospace or real-time optimisation are strongly encouraged to apply.

This is an opportunity to:

• develop genuinely new industrial AI capability;
• work directly with experienced drilling, directional and subsurface specialists;
• contribute to potential intellectual property and future technical publications;
• build systems that could materially improve safety, drilling efficiency and well delivery.

Please send your CV or LinkedIn profile, availability, and a brief description of the hardest technical system or model you have built to: ***email_hidden***

Subject: AI Drilling Programme

A GitHub profile, technical paper, portfolio, architecture diagram or short project summary is welcome—but not mandatory.

The location and engagement model will be discussed with the shortlisted candidates.

Role Description

Drill Insight is seeking a Principal AI/ML Lead to lead the modelling workstream for an intelligent drilling optimisation programme, available on either a hybrid or fully remote basis. The role may be based in Perth, Western Australia, or performed remotely from Thailand, Vietnam, the Philippines, Kuala Lumpur, or India with occasional travel to Australia or client. Candidates based in Australia may also be offered flexible working arrangements, including working from home for part of the week.

Key responsibilities

• Own the end-to-end ML architecture and model-development methodology.

o ROP prediction models

o drilling-hazard and anomaly-detection models

o formation-change detection

o formation-adaptive ML

o bit-walk prediction

o multi-objective parameter optimisation

o features,

o labels,

o model constraints,

o objective functions,

o risk penalties,

o confidence thresholds,

o and recommendation-withdrawal rules.

• Select appropriate approaches across gradient boosting, statistical learning, time-series models, neural networks and probabilistic methods.
• Establish well-based blind testing, model validation and uncertainty quantification.
• Implement model explainability and feature-importance workflows.
• Define model-monitoring, retraining and drift-management criteria.
• Guide the Applied Data Scientist & ML Platform Engineer.
• Work closely with drilling, directional and subsurface specialists.
• Present methodologies, limitations and results clearly to the client.

Qualifications

• Strong Python and applied ML experience.
• Demonstrated ownership of production-grade predictive models.
• Experience with XGBoost, LightGBM, CatBoost, scikit-learn, PyTorch or TensorFlow.
• Strong understanding of time-series modelling, anomaly detection and optimisation.
• Experience with model explainability, uncertainty and blind-test validation.
• Ability to translate domain requirements into mathematical and computational formulations.
• Strong technical communication and documentation skills.

Highly desirable

• Physics-informed or physics-guided ML.
• Multi-objective or Bayesian optimisation.
• Industrial, energy, mining, robotics or process-control experience.
• Experience with high-frequency sensor data.
• Familiarity with drilling parameters such as WOB, RPM, ROP, SPP, torque and flow.

What success looks like

• Models that perform credibly on held-out wells.
• Recommendations that remain inside domain-approved operating limits.
• Clear confidence and explainability outputs.
• A model suite that can be deployed, monitored and progressively retrained.