Senior/Staff Machine Learning Engineer

Terra AI; Palo Alto (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Terra AI in Palo Alto is seeking a talented deep learning engineer to pioneer the development of a generative model for 3D geological applications in the clean energy sector.

The role requires extensive experience with PyTorch and the ability to design and implement large-scale deep learning models. Responsibilities include training diffusion models, curating high-quality datasets, and collaborating with project teams.

Qualifications

• Extensive know-how in PyTorch, including custom modules and debugging.
• Proven experience in designing and training complex deep learning architectures.
• Hands-on skills in creating and maintaining high-quality datasets.
• Strong software engineering practices with code optimization expertise.

Responsibilities

• Design, train, and test diffusion models for 3D geological outputs.
• Develop methods conditioning generation on geophysical data.
• Generate synthetic data enhancing model performance.
• Adapt models for specific projects in collaboration with teams.

Skills

PyTorch Experience

Large Deep Learning Models

Data Curation Skills

Software Engineering

Job description

Role description

In the same way image generators have shown the remarkable ability to produce a diverse set of realistic pictures conditioned on a text prompt (and other inputs), we are developing a generative model that produces 3D geological models conditioned on geophysical surveys, borehole measurements, and other forms of physical observation. The outputs of the generative model capture what we know and don’t know about the state of the subsurface, allowing explorers to make maximally informed decisions about how and where to explore for critical resources.

We are looking for a talented deep learning engineer or scientist to lead the development of this model that will revolutionize decision-making in the earth subsurface for a wide range of clean energy applications.

Role Responsibilities

• Design, train, test, and iterate on diffusion models for 3D geological models
• Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations
• Inform the generation of synthetic data to improve model performance
• Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

Qualifications

Required Qualifications:

• Extensive PyTorch Experience
  • Deep understanding of PyTorch, including writing custom modules, optimizing training, and debugging issues in large-scale models.
• Expertise in Developing Large Deep Learning Models from Scratch
  • Proven ability to design, implement, and train complex deep learning architectures from the ground up.
• Data Curation Skills
  • Hands‑on experience in creating, cleaning, and maintaining high-quality datasets tailored for machine learning applications.
• Strong Software Engineering and Design Experience
  • Proficient in software development best practices, including version control, testing, and code optimization.
  • Familiarity with designing scalable and maintainable systems.

Nice-to-haves

• Experience with Generative Models
  • Familiarity with generative architectures, particularly diffusion models, and an emphasis on posterior sampling methods.
• Knowledge of Transformer Architectures
  • Experience building and training transformers, especially in applications involving 3D data.
• Scaling Models Across Large GPU Clusters
  • Expertise in parallelizing models across multiple GPUs and optimizing distributed training pipelines.
• Cloud Infrastructure Expertise
  • Experience setting up, managing, and optimizing cloud environments for machine learning workloads, including provisioning resources and managing costs.