Senior Machine Learning Scientist

Salt Lake, UT - USA

Monthly Salary: Not Disclosed

Posted on: 8 hours ago

Vacancies: 1 Vacancy

Job Summary

Role Overview

Title: Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences)

Hours: Full-Time Salaried

Location: Salt Lake City UT Hybrid (3 days in office 2 days can be remote)

Benefits Eligible: Yes

Manager: Head of Reservoir R&D

Why we exist

Geothermal energy is the most abundant renewable energy source in the world. There is 2300 times more energy in geothermal heat in the ground than in oil gas coal and methane combined. However historically its been hard to find and expensive to develop. At Zanskar were building technology to find and develop new geothermal resources in order to make geothermal a cheap and vital contributor to a carbon-free electrical grid.

To do that we combine deep subsurface expertise with advanced AI technologiesincluding modern machine learning scalable scientific computing and uncertainty-aware modelingto dramatically improve geothermal discovery and development outcomes. We build systems that can learn from sparse and noisy data emulate expensive physics simulations and help teams make faster higher-confidence decisions about where to drill and how to develop fields.

Who you are

You will help build the modeling and decision-making core of Zanskars geothermal exploration software. This role blends scientific machine learning (surrogate modeling) with sequential decision-making under uncertainty. A successful candidate will:

Explore: youre open-minded about methods and will prototype benchmark and iterate across approaches.

Reproduce & adapt: you can implement ideas from papers and new frameworks quickly then harden the best ones into reliable workflows.

Decision-minded: you care about end-to-end outcomes (value risk time-to-decision) not just model accuracy.

Uncertainty-first: you build models that are accurate well-calibrated and dependable under distribution shift and sparse data regimes.

Collaborative: you work well with domain experts and can translate between geology/engineering intuition and ML systems.

What youll do

Build fast reliable models that emulate or augment computationally expensive physics-based simulations (e.g. reservoir wellbore and coupled multi-physics workflows).

Evaluate and compare multiple modeling approaches (physics-informed operator learning transformers diffusion models etc.) establishing strong baselines and selecting methods based on evidence.

Build multi-step decision systems for exploration and appraisal: POMDP-style planning and belief-space decision making to recommend exploration steps.

Translate scientific and engineering questions into well-defined learning and decision problems: inputs/outputs constraints boundary/initial conditions reward/cost structure and success metrics (e.g. expected NPV probability of success downside risk).

Prototype benchmark and iterate across approaches (POMDP solvers RL methods VOI-style baselines MPC-style replanning) then harden the best ones into reliable workflows and APIs.

Collaborate deeply with geoscientists reservoir engineers and software engineers to integrate these models and policies into production software.

What were looking for

3 years of applied ML experience ideally in scientific ML decision-making under uncertainty surrogate modeling robotics/control or related engineering/science domains.

Expertise in python and modern ML tooling (PyTorch preferred).

Track record of taking models from prototype rigorous evaluation adoption by technical stakeholders.

Strong fundamentals in probability/statistics and comfort with messy real-world scientific datasets.

Experience building or using surrogate models for expensive simulators (PDE-driven systems multi-physics or similar).

Relevant technical strengths

Surrogate modeling.

Sequential decision-making under uncertainty and reinforcement learning.

Software engineering: Git code review reproducibility CI basics Docker/container workflows.

Experience with diffusion models.

Exposure to subsurface modeling domains: geothermal oil & gas CCS hydrogeology geoscience or related.

Familiarity with cloud infrastructure and data systems (SQL object storage orchestration).

Location and Benefits

This position is based out of our headquarters in Salt Lake City Utah and is hybrid.

Benefits include:

Paid holidays

15 days PTO PTO 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

Equal Opportunity Employer

Zanskar is an equal-opportunity employer and complies with all applicable federal state and local fair employment practice laws.

Required Experience:

Senior IC

Role OverviewTitle: Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences)Hours: Full-Time SalariedLocation: Salt Lake City UT Hybrid (3 days in office 2 days can be remote)Benefits Eligible: YesManager: Head of Reservoir R&DWhy we existGeothermal energy is t...