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Geometric Deep Learning Jobs (NOW HIRING)

Apple Inc.

CV and ML Engineer

Seattle, WA · On-site

$175 - $308.50/hr

Experience in deep learning with demonstrated work in 3D vision or geometric deep learning * Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX * Experience with ...

Apple

CVML Engineer - 3D Reconstruction

Sunnyvale, CA · On-site

$150K - $277K/yr

Background in differentiable optimization and geometric deep learning approaches. Experience optimizing ML models for mobile deployment and resource-constrained environments. Knowledge of SLAM ...

Apple

CVML Engineer - 3D Reconstruction

Sunnyvale, CA · On-site

$216K - $324K/yr

Background in differentiable optimization and geometric deep learning approaches. Experience optimizing ML models for mobile deployment and resource-constrained environments. Knowledge of SLAM ...

Apple

CV and ML Engineer

Seattle, WA

$175K - $308K/yr

... or geometric deep learning Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX Experience with rapid prototyping, reproduction, and validation of research ideas ...

Apple

CV and ML Engineer

Seattle, WA

$175K - $308K/yr

... or geometric deep learning Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX Experience with rapid prototyping, reproduction, and validation of research ideas ...

Apple Inc.

CV and ML Engineer

Sunnyvale, CA · On-site

$175 - $309/hr

Experience in deep learning with demonstrated work in 3D vision or geometric deep learning * Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX * Experience with ...

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 ...

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All JobsGeometric Deep Learning Jobs

Geometric Deep Learning information

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$11K

$83.9K

$140K

How much do geometric deep learning jobs pay per year?

As of Sep 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 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.

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 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.

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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?

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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, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Postdoctoral Fellow in Biomedical Informatics (Cai Lab)

Harvard University

Cambridge, MA • On-site

$54K - $73K/yr

Full-time

Re-posted 17 days ago

Harvard University rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

82nd of 630 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral Fellow in Biomedical Informatics (Cai Lab)
School
Harvard Medical School
Department/Area
Biomedical Informatics
Position Description
A Postdoctoral Research Fellow position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic research group focused on several synergistic goals: generating actionable Real-World Evidence (RWE) from multi-institutional Electronic Health Records (EHR), improving the generalizability of clinical evidence across diverse populations using multi-source and multi-modal data, and accelerating drug discovery by leveraging these rich, integrated datasets. This role offers a unique opportunity to develop methodological innovations that bridge the gap between computational theory and impactful clinical application.
We are seeking a highly motivated individual with a strong statistical and machine learning background. The ideal candidate will have existing expertise in several of the following areas, aligned with our research focus: 1) Causal inference, invariant learning and representation learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing.
Basic Qualifications
Candidates must hold a Ph.D. in a quantitative field, such as statistics, biostatistics, computer science, or a related discipline. Success in this position requires strong quantitative research capabilities and demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must possess excellent written and oral communication abilities to effectively disseminate research findings and collaborate within a multidisciplinary team.
Additional Qualifications
Special Instructions
Contact Information
Mo Moro
Contact Email
mohammed_moro@hms.harvard.edu
Salary Range
Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines
Minimum Number of References Required
Maximum Number of References Allowed
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