Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar). * Familiarity with generative modeling: diffusion models, flow matching, score ...
Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar). * Familiarity with generative modeling: diffusion models, flow matching, score ...
Postdoctoral Fellow, Structural Biology
Buffalo, NY · On-site
$70K - $85K/yr
Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3). * Familiarity with Docker/Singularity for reproducible HPC environments.
Postdoctoral Fellow, Structural Biology
Buffalo, NY · On-site
$70K - $85K/yr
Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3). * Familiarity with Docker/Singularity for reproducible HPC environments.
Machine Learning Engineer, Perception
Columbus, OH · On-site +1
$100K - $138K/yr
Design and lead experiments evaluating state-of-the-art deep learning models, including transformer-based and geometric feature learning architectures * Design and lead real-time perception systems ...
Machine Learning Engineer, Perception
Columbus, OH · On-site +1
$100K - $138K/yr
Design and lead experiments evaluating state-of-the-art deep learning models, including transformer-based and geometric feature learning architectures * Design and lead real-time perception systems ...
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 ...
New
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 ...
New
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 ...
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 ...
Expertise with state-of-the-art AI/ML models such as generative models (diffusion), language models (transformers), multimodal learning, geometric deep learning, graph representation learning * Deep ...
Expertise with state-of-the-art AI/ML models such as generative models (diffusion), language models (transformers), multimodal learning, geometric deep learning, graph representation learning * Deep ...
Expertise with state-of-the-art AI/ML models such as generative models (diffusion), language models (transformers), multimodal learning, geometric deep learning, graph representation learning * Deep ...
Expertise with state-of-the-art AI/ML models such as generative models (diffusion), language models (transformers), multimodal learning, geometric deep learning, graph representation learning * Deep ...
Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces. * Models that combine prediction ...
Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces. * Models that combine prediction ...
Research Scientist - Frontier AI/ML & Quantum Algorithms
San Francisco, CA · On-site
$200K - $300K/yr
Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces. * Models that combine prediction ...
Research Scientist - Frontier AI/ML & Quantum Algorithms
San Francisco, CA · On-site
$200K - $300K/yr
Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces. * Models that combine prediction ...
ML Research Scientist - Atomistic Foundation Models
New York, NY · On-site
$164K - $259K/yr
Proven expertise in deep generative modeling (e.g., diffusion, VAEs, flows, autoregressive transformers). * Experience in representation learning for structured data, especially graph or 3D geometric ...
ML Research Scientist - Atomistic Foundation Models
New York, NY · On-site
$164K - $259K/yr
Proven expertise in deep generative modeling (e.g., diffusion, VAEs, flows, autoregressive transformers). * Experience in representation learning for structured data, especially graph or 3D geometric ...
Software Engineer, Geometric Vision, Tesla AI
Palo Alto, CA · On-site
$176K - $420K/yr
As a key contributor, you will drive innovation in geometric vision and 3D foundation model ... and deep learning, with hands-on implementation experience * Expertise in key areas of computer ...
Software Engineer, Geometric Vision, Tesla AI
Palo Alto, CA · On-site
$176K - $420K/yr
As a key contributor, you will drive innovation in geometric vision and 3D foundation model ... and deep learning, with hands-on implementation experience * Expertise in key areas of computer ...
... deep learning models for production inference, including quantization and batching. • Deploy and ... DGL, PyTorch Geometric, NetworkX). • Knowledge of GPU programming (CUDA) and performance ...
... deep learning models for production inference, including quantization and batching. • Deploy and ... DGL, PyTorch Geometric, NetworkX). • Knowledge of GPU programming (CUDA) and performance ...
Senior Director & Product Lead
Waltham, MA · Hybrid
$251K - $263K/yr
You can go toe-to-toe with Staff Engineers and Data Scientists on topics like geometric deep learning, native 3D geometry processing, and cloud-to-enterprise security architectures. * "Super IC ...
Senior Director & Product Lead
Waltham, MA · Hybrid
$251K - $263K/yr
You can go toe-to-toe with Staff Engineers and Data Scientists on topics like geometric deep learning, native 3D geometry processing, and cloud-to-enterprise security architectures. * "Super IC ...
Senior Director & Product Lead
North Bethesda, MD · Hybrid
$233K - $244K/yr
You can go toe-to-toe with Staff Engineers and Data Scientists on topics like geometric deep learning, native 3D geometry processing, and cloud-to-enterprise security architectures. * "Super IC ...
Senior Director & Product Lead
North Bethesda, MD · Hybrid
$233K - $244K/yr
You can go toe-to-toe with Staff Engineers and Data Scientists on topics like geometric deep learning, native 3D geometry processing, and cloud-to-enterprise security architectures. * "Super IC ...
... deep learning models for production inference, including quantization and batching. • Deploy and ... DGL, PyTorch Geometric, NetworkX). • Knowledge of GPU programming (CUDA) and performance ...
... deep learning models for production inference, including quantization and batching. • Deploy and ... DGL, PyTorch Geometric, NetworkX). • Knowledge of GPU programming (CUDA) and performance ...
Develop deep learning models for 2D and 3D object detection, including implementation and ... geometric transformations. Design and implement multi-object tracking systems using Kalman ...
Develop deep learning models for 2D and 3D object detection, including implementation and ... geometric transformations. Design and implement multi-object tracking systems using Kalman ...
Software Engineer, Applied AI
Washington, DC · On-site
$165K - $250K/yr
... geometric deep learning, large language models (LLM), and generative AI * Ability to operate a Vector Database * Ability to program in TypeScript and Python * Ability to pre-train and fine tune large ...
Software Engineer, Applied AI
Washington, DC · On-site
$165K - $250K/yr
... geometric deep learning, large language models (LLM), and generative AI * Ability to operate a Vector Database * Ability to program in TypeScript and Python * Ability to pre-train and fine tune large ...
Machine Learning Lead
Austin, TX · On-site +1
$54.75 - $75/hr
... Deep expertise with Graph Neural Networks (PyTorch Geometric, DGL) for relational reasoning Strong foundation in Transformer architectures and attention mechanisms Hands‐on experience with ...
Machine Learning Lead
Austin, TX · On-site +1
$54.75 - $75/hr
... Deep expertise with Graph Neural Networks (PyTorch Geometric, DGL) for relational reasoning Strong foundation in Transformer architectures and attention mechanisms Hands‐on experience with ...
Staff Machine Learning Systems Engineer
$230K - $322K/yr
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure ... Geometric, Deep Graph Library) is a big plus Pay Transparency: This job posting may span more than ...
Staff Machine Learning Systems Engineer
$230K - $322K/yr
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure ... Geometric, Deep Graph Library) is a big plus Pay Transparency: This job posting may span more than ...
Deep expertise in Geometric Deep Learning, Computer Vision (3D mesh/B-Rep processing), or Generative AI is highly preferred given the focus on native 3D geometry. * Strategic Delivery: Proven ability ...
Deep expertise in Geometric Deep Learning, Computer Vision (3D mesh/B-Rep processing), or Generative AI is highly preferred given the focus on native 3D geometry. * Strategic Delivery: Proven ability ...
Geometric Deep Learning information
See salary details
$21.8K is the 25th percentile. Wages below this are outliers.
$11K - $22.7K
27% of jobs
$22.7K - $34.5K
0% of jobs
$34.5K - $46.2K
0% of jobs
$46.2K - $57.9K
0% of jobs
$57.9K - $69.6K
0% of jobs
The median wage is $80.4K / yr.
$69.6K - $81.4K
25% of jobs
$81.4K - $93.1K
18% of jobs
$101.5K is the 75th percentile. Wages above this are outliers.
$93.1K - $104.8K
7% of jobs
$104.8K - $116.5K
2% of jobs
$116.5K - $128.3K
0% of jobs
$128.3K - $140K
21% of jobs
$11K
$83.9K
$140K
How much do geometric deep learning jobs pay per year?
As of Jun 5, 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 geometric deep learning?
Geometric deep learning is a field of machine learning that focuses on the design of neural network architectures capable of processing data with non-Euclidean structures, such as graphs, manifolds, and point clouds. Unlike traditional deep learning methods, which work well with grid-like data such as images, geometric deep learning tackles challenges where data has more complex, irregular structures. Applications include social network analysis, 3D shape recognition, drug discovery, and recommendation systems. The field aims to generalize deep learning techniques to data that is best represented by geometric or topological constructs.
What is the difference between Geometric Deep Learning vs Data Scientist?
| Aspect | Geometric Deep Learning | Data Scientist |
|---|---|---|
| Required Credentials | Advanced degrees in computer science, machine learning, or related fields | Bachelor's or master's in data science, statistics, or related fields |
| Work Environment | Research labs, AI development teams, academia | Business analytics, product teams, consulting firms |
| Industry Usage | AI, robotics, computer vision, graph analysis | Business 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.
More about Geometric Deep Learning jobs
What cities are hiring for Geometric Deep Learning jobs? Cities with the most Geometric Deep Learning job openings:
What states have the most Geometric Deep Learning jobs? States with the most job openings for Geometric Deep Learning jobs include:
What job categories do people searching Geometric Deep Learning jobs look for? The top searched job categories for Geometric Deep Learning jobs are:
Full-time
Posted 7 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 applied machine learning research and rigorous software engineering. You will advance the state of the art in foundation simulation models by implementing and experimenting with internal and literature-sourced ideas, participating with research teams to scale our ML systems, train and evaluate models, and engineer scientific prototypes into production.
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
About You
Nice to Have
Even if you hit none of these bonus features, we encourage you to apply!
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 applied machine learning research and rigorous software engineering. You will advance the state of the art in foundation simulation models by implementing and experimenting with internal and literature-sourced ideas, participating with research teams to scale our ML systems, train and evaluate models, and engineer scientific prototypes into production.
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
- Design and run experiments to test out hypotheses on the path to foundation model development.
- Engineer meaningful evals and metrics which enable rapid model iteration.
- Design, build and maintain scalable, reproducible libraries for training, experimentation evaluation, and simulation, in service of large-scale research initiatives.
- Implement model architectures both from the literature and developed in collaboration with our in-house researchers that push the boundaries of molecular simulation.
- Enable agent-driven research and workflows and maintain guardrails on agentic tooling.
- Help prepare manuscripts, software artifacts, and datasets for public release.
About You
- Strong software engineering fundamentals, with experience not just building one-off scripts but reproducible pipelines for research, writing necessary documentation, and observing coding best-practices.
- Track record of observable artifacts (e.g., GitHub, papers) showing work in ML or scientific computing libraries.
- Solid working knowledge of PyTorch and JAX and the modern ML research stack.
- Comfortable with HPC or large-scale compute environments, and used to thinking on the scale of hundreds or thousands (or even more!) fits running at once.
- Sufficient scientific depth to engage with the research questions, whether developed through prior industry experience or during a PhD.
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).
- Familiarity with generative modeling: diffusion models, flow matching, score-based methods.
- Regular involvement in open-source ML or scientific computing libraries.
- Experience building agent-driven research, active learning, and data curation pipelines.