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Geometric Deep Learning Jobs (NOW HIRING)

Hadrian Automation

Data Scientist

Torrance, CA · On-site

$170K - $300K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Geometric deep learning: mesh / point-cloud networks, GNNs, PyTorch Geometric * CAD / B-rep, feature recognition, and turning part geometry into ML features * Retrieval and ANN at scale; embedding ...

Visible Hands

Machine Learning Engineer

Manhattan, NY · On-site

$150 - $190/hr

Design and train deep learning models. * Build the ECAD Data Pipeline: Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP ...

Root Access, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$140 - $190/hr

Design and train deep learning models. * Build the ECAD Data Pipeline: Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP ...

Multicoin

Data Scientist

Los Angeles, CA · On-site

$170 - $300/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Geometric deep learning: mesh / point‑cloud networks, GNNs, PyTorch Geometric * CAD / B‑rep, feature recognition, and turning part geometry into ML features * Retrieval and ANN at scale ...

Hadrian Automation

Data Scientist

Torrance, CA · On-site

$170 - $300/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Geometric deep learning: mesh / point-cloud networks, GNNs, PyTorch Geometric * CAD / B-rep, feature recognition, and turning part geometry into ML features * Retrieval and ANN at scale; embedding ...

Hadrian Automation

Data Scientist

Santa Monica, CA · On-site

$170 - $300/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Geometric deep learning: mesh / point-cloud networks, GNNs, PyTorch Geometric * CAD / B-rep, feature recognition, and turning part geometry into ML features * Retrieval and ANN at scale; embedding ...

Hadrian Automation, Inc

Data Scientist

Los Angeles, CA · On-site

$170K - $300K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Geometric deep learning: mesh / point-cloud networks, GNNs, PyTorch Geometric * CAD / B-rep, feature recognition, and turning part geometry into ML features * Retrieval and ANN at scale; embedding ...

P-1 AI

AI Research Scientist - Spatial Reasoning

San Francisco, CA · On-site

$200K - $385K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Have conducted research in one or more areas such as spatial reasoning, geometric deep learning, 3D vision, multimodal reasoning, world models, robotics, simulation, or related fields. * Are fluent ...

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Showing results 41-60

All JobsGeometric Deep Learning Jobs

Geometric Deep Learning information

See salary details

$11K

$83.9K

$140K

How much do geometric deep learning jobs pay per year?

As of Aug 16, 2026, the average yearly pay for geometric deep learning in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

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?

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.
More about Geometric Deep Learning jobs

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What states have the most Geometric Deep Learning jobs?

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Geometric Deep Learning jobs near you
Infographic showing various Geometric Deep Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Machine Learning Research Engineer (MLRE) - Workflows/Systems

Achira

San Francisco, CA • On-site

$120 - $160/hr

Other

Re-posted 11 days ago

Job description

Why Achira

At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.

At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.

About the Role

We\'re looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models.

While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.

What You\’ll Do

  • Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack.

  • Rationalize machine learning systems design and software architecture.

  • Identify blockers and build solutions that scale to the size of foundation models.

  • Operate as the glue between research scientists and the infrastructure team.

About You

  • At least two years relevant industry experience.

  • Highly fluent in and enthusiastic about PyTorch and JAX.

  • Used to thinking in asynchronous primitives.

  • Strong views on library design: clean abstractions, minimal surface area, consistency.

  • Solid track record of observable artifacts (e.g., GitHub) showing clear, well-documented code.

  • ML generalist who knows what scalable, reliable ML systems look like.

Nice to Have

Even if you hit none of these bonus features, we encourage you to apply!

  • Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery.

  • Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc.

  • Lack of fear around interacting with quantum chemical scientists and their data pipelines.

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