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Geometric Deep Learning Jobs (NOW HIRING)

Machine Learning Engineer

San Mateo, CA · On-site

$110K - $165K/yr

Evaluate emerging research in areas such as sequence modeling, geometric deep learning, representation learning, and foundation models. * Data & Model Infrastructure * Build and maintain scalable ...

New

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

... geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; * generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non ...

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

... 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 ...

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Geometric Deep Learning information

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$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?

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.
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:
Infographic showing various Geometric Deep Learning job openings in the United States as of May 2026, with employment types broken down into 97% Full Time, and 3% Part Time. 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.
Machine Learning Engineer

Machine Learning Engineer

Advatix, Inc.

San Mateo, CA • On-site

$110K - $165K/yr

Full-time

Medical, Dental, Vision, PTO

This job post has expired today. Applications are no longer accepted.


Job description

Job Title: Machine Learning Engineer / Research Engineer
Pay: $$110,000 – $165,000 Base Salary + Equity
Shift: N/A
Location: San Mateo, CA (Peninsula) – Onsite Preferred
Schedule: Full time, Permanent Role
Visa Sponsorship: Not Available
Relocation Assistance: Not Available
Job Overview
About the Role
  • We are looking for a highly skilled Machine Learning Engineer / Research Engineer to join our founding team and help develop intelligent systems that transform how hardware and mechanical engineers design products.
  • This is a unique opportunity to work at the intersection of cutting-edge machine learning research and real-world engineering applications. You'll collaborate directly with founders, engineers, and customers to design, train, deploy, and continuously improve machine learning systems that accelerate CAD workflows and hardware design.
  • As one of the earliest ML hires, you will have significant ownership over technical direction, architecture decisions, and the long-term evolution of our AI platform.

  • Key Responsibilities
  • Machine Learning Research & Development
  • Design, train, and optimize custom deep learning models that understand CAD workflows and generate intelligent next-step design recommendations.
  • Develop novel machine learning approaches for geometry, design, and engineering-related datasets.
  • Evaluate emerging research in areas such as sequence modeling, geometric deep learning, representation learning, and foundation models.
  • Data & Model Infrastructure
  • Build and maintain scalable Python-based training, evaluation, and experimentation pipelines.
  • Transform complex, real-world CAD and geometry data into high-quality training datasets and signals.
  • Implement robust offline and online evaluation frameworks to measure model performance and business impact.
  • Production ML Systems
  • Own the complete ML lifecycle from research and prototyping through deployment, monitoring, and optimization.
  • Architect model-serving infrastructure and backend components that enable fast, reliable integration into CAD environments.
  • Establish best practices for experimentation, logging, model versioning, and performance monitoring.
  • Cross-Functional Collaboration
  • Work closely with founders, mechanical engineers, hardware engineers, and early customers to understand workflows and translate them into ML solutions.
  • Collaborate with backend engineers on APIs, infrastructure, data models, and platform scalability.
  • Help define the long-term strategy for applying machine learning to hardware and CAD design.

  • Required Qualifications
  • Machine Learning Expertise
  • 4+ years of hands-on machine learning experience in industry, research, or a combination of both.
  • Equivalent Master's or PhD research experience will be considered.
  • Demonstrated success designing, training, improving, and deploying machine learning models—not simply utilizing hosted AI APIs.
  • Deep Learning & Research
  • Expert-level proficiency with PyTorch (preferred) or similar frameworks such as TensorFlow or JAX.
  • Experience implementing custom architectures, loss functions, optimization methods, and training loops.
  • Strong understanding of model evaluation, experimentation, and performance trade-offs.
  • Software Engineering
  • Strong Python programming skills with experience building production-ready systems.
  • Ability to write clean, maintainable, and well-tested code with appropriate documentation and abstractions.
  • Experience developing scalable ML infrastructure and backend services.
  • Ownership & Execution
  • Proven ability to independently drive projects from concept through deployment.
  • Experience building end-to-end ML systems including data pipelines, experimentation frameworks, model training, deployment, and monitoring.
  • Comfortable solving ambiguous, open-ended technical problems.
  • Communication & Collaboration
  • Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
  • Experience working cross-functionally with engineers, product teams, researchers, and customers.
  • Startup Mindset
  • Thrives in fast-paced, high-ownership environments.
  • Comfortable wearing multiple hats across machine learning, research, backend engineering, and infrastructure.

  • Preferred Qualifications
  • Published research papers or meaningful open-source contributions demonstrating novel technical work.
  • Experience with:
  • CAD systems and workflows
  • Computational geometry
  • Computer graphics
  • 3D representations
  • Robotics
  • Familiarity with cloud ML infrastructure (AWS, GCP).
  • Experience with backend frameworks such as FastAPI, Flask, or Django.

  • Ideal Candidate
  • You are a fundamentally strong machine learning builder who cares equally about theory and production. You enjoy reading research papers, developing novel approaches, and translating ideas into production systems that create measurable impact for users.
  • You are excited about solving challenging problems in AI-powered engineering software and want to help build a category-defining product from the ground up.

  • Must-Have Requirements
  • Must be based in the United States and possess valid work authorization.
  • Strong proficiency in Python and modern deep learning frameworks (PyTorch preferred).
  • Demonstrated experience building and deploying custom machine learning models from scratch.
  • Experience designing architectures, creating training pipelines, and shipping ML features to production.
  • Minimum 4 years of relevant industry or equivalent academic experience.

  • Benefits & Perks
  • Competitive salary ($110,000 – $165,000)
  • Meaningful equity ownership
  • Comprehensive medical, dental, and vision insurance
  • Catered team lunches at the San Mateo office
  • Unlimited / flexible paid time off
  • High-impact role within a YC-backed startup
  • Direct collaboration with experienced founders and engineers
  • Significant opportunities for growth, learning, and career advancement
  • Opportunity to help define the future of AI-powered CAD and hardware design

HRforGrowth is an extension of the Growth Catalyst Group (GCG), a partnership of companies with more than 65 years of operating experience and a history of successfully serving customers across industries and disciplines. 
GCG® is one of the world’s leading providers of business transformation solutions related to supply chain and technology solutions for order fulfillment and marketing execution. We are committed to an inclusive workplace that does not discriminate against race, nationality, religion, age, marital status, physical or mental disability, sexual orientation, gender, or gender identity. We believe in diversity and encourage any qualified individual to apply. We are an EEOC Employer. 

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