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Geometric Deep Learning Jobs in California (NOW HIRING)

Mach9 Robotics Inc

Head of ML

San Francisco, CA · On-site

Background in machine learning for 3D perception - point cloud understanding, 3D detection/segmentation, geometric deep learning, or related areas. * Experience with CAD AI, design automation, or ...

Astera

General DiffUSE Job Application

Emeryville, CA · On-site

$100K - $300K/yr

ML on raw experimental data rather than processed structures * 3D vision and geometric deep learning backgrounds especially welcome Dataset generation and open release * Designing and running ...

Altera

Senior MLOps & AI Infrastructure Engineer

San Jose, CA · On-site

$127K - $172K/yr

... or geometric deep learning for circuit and netlist analysis • Background in reinforcement learning for optimization problems • Exposure to zero-trust security, DevSecOps, and compliance ...

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Geometric Deep Learning information

What is geometric deep learning?

Geometric deep learning is a branch of machine learning focused on designing neural networks that operate on non-Euclidean data such as graphs and manifolds. It involves techniques like graph neural networks and requires understanding of both deep learning and geometric structures, often using tools like PyTorch or TensorFlow. Professionals in this field develop models for applications like social network analysis, 3D shape recognition, and molecular modeling.

What is the difference between Geometric Deep Learning vs Data Scientist?

AspectGeometric Deep LearningData Scientist
Required CredentialsAdvanced degrees in computer science, machine learning, or related fieldsBachelor's or master's in data science, statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness analytics, product teams, consulting firms
Industry UsageAI, robotics, computer vision, graph analysisBusiness intelligence, marketing, finance, healthcare

Geometric Deep Learning focuses on applying deep learning techniques to non-Euclidean data like graphs and manifolds, often requiring advanced technical skills. Data Scientists analyze and interpret data to inform business decisions, typically working with structured data and statistical tools. While both roles involve data analysis, Geometric Deep Learning is more research-oriented and specialized in AI development, whereas Data Scientists focus on practical data insights across industries.

What are some common challenges faced when working on Geometric Deep Learning projects, and how can they be addressed?

One common challenge in Geometric Deep Learning is dealing with the complexity and diversity of data structures, such as graphs, point clouds, or manifolds. These data types often require specialized neural network architectures and custom preprocessing steps, which can be more complex than traditional deep learning tasks. Collaboration with domain experts and staying updated with the latest research are crucial for overcoming these obstacles. Additionally, debugging and visualizing the learning process can be more challenging, so employing robust evaluation metrics and visualization tools is highly recommended.

What are the key skills and qualifications needed to thrive as a Geometric Deep Learning Engineer, and why are they important?

To excel as a Geometric Deep Learning Engineer, you need a strong background in mathematics, machine learning, and computer science, typically supported by an advanced degree in a related field. Proficiency with deep learning frameworks like PyTorch or TensorFlow, as well as experience with graph neural networks (GNNs) and geometric data structures, is essential. Strong analytical thinking, problem-solving abilities, and collaborative communication are key soft skills for innovating and working with interdisciplinary teams. These skills are crucial for developing cutting-edge models that leverage geometric data, enabling impactful solutions across domains such as computer vision, biology, and social network analysis.

Which 5 jobs will survive AI?

Geometric Deep Learning specialists are likely to continue in demand due to their expertise in advanced neural network architectures and 3D data processing. Jobs involving complex problem-solving, creativity, and domain-specific knowledge—such as data scientists, AI researchers, software engineers, cybersecurity analysts, and healthcare professionals—are expected to persist as AI tools augment rather than replace these roles. Continuous learning and proficiency with AI frameworks like TensorFlow or PyTorch enhance job security in these fields.

What engineer makes $500,000 a year?

Senior engineers in specialized fields such as software engineering, data engineering, or machine learning engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries like technology or finance. These roles often require expertise in programming, system design, and sometimes leadership or management responsibilities.
What are popular job titles related to Geometric Deep Learning jobs in California? For Geometric Deep Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Geometric Deep Learning jobs in California look for? The top searched job categories for Geometric Deep Learning jobs in California are:
What cities in California are hiring for Geometric Deep Learning jobs? Cities in California with the most Geometric Deep Learning job openings:
Geometric Deep Learning jobs near you
Infographic showing various Geometric Deep Learning job openings in California as of June 2026, with employment types broken down into 9% Internship, and 91% Full Time. Highlights an 96% In-person, and 4% Remote job distribution.
Senior Software Engineer - cuEquivariance

Senior Software Engineer - cuEquivariance

Nvidia Corporation

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Posted 15 days ago

Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join our group and discover how you can develop a lasting impact on the world.
NVIDIA BioNeMo is building the computational foundation for the next generation of biological discovery. We are looking for a Senior Software Engineer to join the cuEquivariance team - an NVIDIA library that accelerates geometric neural networks on NVIDIA GPUs, enabling researchers in molecular biology, materials science, and physics to train and deploy equivariant models at scale. This team builds and ships the production GPU kernels and software interfaces that power equivariant deep learning throughout the scientific field. The work spans CUDA kernel engineering, Python library development involving both PyTorch and JAX, and direct collaboration with research teams and external framework developers. If you want to work where GPU computing meets graph-based deep learning, this is the role for you. Your work will run in production pipelines across the scientific community.
What You Will Be Doing:
  • Build, implement, and optimize CUDA kernels for equivariant neural network primitives - tensor products, segmented polynomials, and triangle-based operations - targeting peak performance across NVIDIA GPU generations.
  • Be responsible for the end-to-end delivery of GPU-accelerated geometric ML primitives: from implementation to validated, production-quality software that external frameworks depend on.
  • Build and maintain the interfaces for PyTorch and JAX that expose cuEquivariance primitives to application developers and researchers.
  • Drive CI/CD infrastructure for multi-GPU kernel builds, automated correctness testing, and performance regression tracking.
  • Collaborate with Applied Science and research teams to evaluate new equivariant architectures and translate prototypes into production kernels.
  • Engage directly with third-party framework developers and partners to align on interfaces and ensure delivered software integrates cleanly into production pipelines.

What We Need to See:
  • 6+ years of software engineering experience with a strong background in CUDA and GPU programming.
  • Deep proficiency in C++ and Python; experience building and shipping production libraries used by external developers.
  • Good foundation in GPU computing: memory hierarchy, warp-level execution, occupancy, and performance profiling methodology.
  • Experience building or chipping in to production scientific software libraries, ML frameworks, or developer-facing GPU APIs.
  • Familiarity with concepts in geometric machine learning - equivariance, group representations, irreducible representations, or tensor products - sufficient to work efficiently in the domain.
  • BS/MS in Computer Science, Physics, Applied Mathematics, or a related field, or equivalent experience.

Ways to Stand Out from the Crowd:
  • You have chipped in to or deeply used a major neural network framework that respects equivariance: e3nn, MACE, NequIP, SE(3)-Transformers, or similar.
  • Hands-on experience with Triton kernel development or other GPU kernel authoring tools alongside CUDA.
  • Experience with mixed-precision or tensor-core-aware algorithm design for scientific or ML workloads.
  • PhD or equivalent experience in computational chemistry, biophysics, physics, or computer science with a focus on geometric deep learning or HPC.
  • Contributions to open-source geometric ML or GPU computing projects.

Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 26, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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