Senior/Staff Machine Learning Engineer

About the role

This role builds a generative model for 3D geological modeling at the intersection of AI and geoscience. The position combines deep learning with subsurface domain expertise to support critical resource exploration.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Condition subsurface representations on geophysical surveys and borehole measurements using a generative model, enabling clearer evaluation of multiple exploration scenarios.

Capture what is known and unknown about the subsurface by producing 3D geological models, supporting confident decision-making for critical resource exploration.

Generate and incorporate synthetic data to improve model performance and generalization when labeled subsurface data are limited.

Align diffusion modeling workflows with project teams to address real-world exploration constraints and objectives in clean energy and mining contexts.

Requirements

Write custom PyTorch modules, optimize training, and debug large-scale models to build reliable systems for complex geological data.

Design and implement deep learning architectures from scratch to tackle challenging subsurface problems where off-the-shelf models are insufficient.

Create, clean, and maintain high-quality datasets that support machine learning applications in subsurface exploration.

Apply software development best practices, including version control, testing, and code optimization, to develop maintainable pipelines.

Structure models and pipelines to scale across demanding exploration workflows, ensuring robustness and efficiency.

Nice to have

Employ generative architectures, especially diffusion models and posterior sampling methods, to align with uncertainty-aware subsurface decision-making.

Build and train transformer models for structured 3D data, providing alternative and complementary approaches to geological representation.

Parallelize models across multiple GPUs and optimize distributed training pipelines to accelerate experimentation cycles.

Configure and manage cloud environments for machine learning workloads, including resource provisioning and cost control for sustained training and inference.

Practical notes

The role operates remotely across the US within a partnership-focused culture. Collaboration with geoscience and project teams is expected in clean energy and mining contexts. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Machine learning roles of this level typically require fluency with model training frameworks and data pipelines. Diffusion models and probabilistic generative methods are common tools for uncertain domains. Distributed training on GPU clusters and cloud infrastructure management are standard practices in large-scale model development.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.