Job Description
The Senior Machine Learning Scientist will develop and implement advanced AI models and decision-making systems to improve geothermal resource discovery and development, collaborating with geoscientists and engineers to create reliable workflows that emulate physics-based simulations and optimize exploration strategies.
Key Responsibilities
- Build models that emulate or augment physics-based simulations for geothermal exploration.
- Evaluate and compare various modeling approaches to select the most effective methods.
- Develop multi-step decision systems for exploration and appraisal using POMDP-style planning and belief-space decision making.
- Translate scientific and engineering questions into well-defined learning and decision problems.
- Prototype, benchmark, and iterate on decision-making approaches such as POMDP solvers, reinforcement learning, and MPC, then implement reliable workflows and APIs.
- Collaborate with geoscientists, reservoir engineers, and software engineers to integrate models and policies into production software.
Requirements
- At least 3 years of applied machine learning experience, ideally in scientific ML, decision-making under uncertainty, surrogate modeling, robotics control, or related engineering science domains.
- Proficiency in Python and modern ML tooling, with a preference for PyTorch.
- A track record of taking models from prototype through rigorous evaluation to adoption by technical stakeholders.
- Strong fundamentals in probability and statistics, with comfort working with messy, real-world scientific datasets.
- Experience building or using surrogate models for expensive simulators such as PDE-driven systems, multi-physics, or similar complex models.
- Technical expertise in surrogate modeling, sequential decision-making under uncertainty, and reinforcement learning.
- Experience with software engineering practices including Git, code review, reproducibility, continuous integration basics, and Docker container workflows.
- Experience with diffusion models.
- Exposure to subsurface modeling domains such as geothermal, oil and gas, CCS, hydrogeology, or related geoscience fields.
- Familiarity with cloud infrastructure and data systems including SQL, object storage, and orchestration tools.
Benefits & Perks
Paid holidays
15 days PTO, with accrual increase based on tenure
Medical, dental and vision coverage
401k
Stock options
Growth opportunities at a company with a direct impact in displacing carbon emissions
Hybrid work schedule (3 days in office, 2 days remote)
Ready to Apply?
Join Zanskar and make an impact in renewable energy
Stay Updated on Sustainability Jobs
Get the latest renewable energy jobs and career tips delivered to your inbox.
Job Alerts
Get notified about new sustainability jobs