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Geometric Deep Learning Jobs in Round Rock, TX (NOW HIRING)

Cisco

Applied AI Scientist

Austin, TX

Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE ... Hands-on experience with frameworks such as PyTorch Geometric (PyG), DGL, GraphGym, GraphML systems ...

Varsity Tutors

Math 1 Tutor

Round Rock, TX · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... Deep knowledge of linear equations and inequalities, functions and function families, systems of ...

Varsity Tutors

Math 1 Tutor

Austin, TX · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... Deep knowledge of linear equations and inequalities, functions and function families, systems of ...

Varsity Tutors

Geometry Tutor

Round Rock, TX · Remote

$18 - $40/hr

Deep knowledge of geometric proofs, triangle congruence and similarity, circles, polygons ... Ability to adapt to different learning styles and student needs. Ways To Connect With Students * 1 ...

Varsity Tutors

Geometry Tutor

Austin, TX · Remote

$18 - $40/hr

Deep knowledge of geometric proofs, triangle congruence and similarity, circles, polygons ... Ability to adapt to different learning styles and student needs. Ways To Connect With Students * 1 ...

Varsity Tutors

HSPT Math Tutor

Round Rock, TX · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... Deep knowledge of HSPT Mathematics section content including arithmetic, algebra, geometry ...

Varsity Tutors

Math 3 Tutor

Round Rock, TX · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... Deep knowledge of polynomial and rational functions, logarithmic and exponential modeling ...

Varsity Tutors

Math 2 Tutor

Austin, TX · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... Deep knowledge of quadratic functions and equations, similarity and right triangle trigonometry ...

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All JobsGeometric Deep Learning JobsGeometric Deep Learning Jobs in Round Rock, TX

Geometric Deep Learning information

See Round Rock, TX salary details

$10.3K

$78.2K

$130.6K

How much do geometric deep learning jobs pay per year?

As of Sep 6, 2026, the average yearly pay for geometric deep learning in Round Rock, TX is $78,226.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,100.00 and $129,600.00 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.

What are popular job titles related to Geometric Deep Learning jobs in Round Rock, TX?

For Geometric Deep Learning jobs in Round Rock, TX, the most frequently searched job titles are:

What job categories do people searching Geometric Deep Learning jobs in Round Rock, TX look for?

The top searched job categories for Geometric Deep Learning jobs in Round Rock, TX are:

What cities near Round Rock, TX are hiring for Geometric Deep Learning jobs?

Cities near Round Rock, TX with the most Geometric Deep Learning job openings:

Geometric Deep Learning jobs near you

Graduate Student Intern - Software Engineering

Cadence Design Systems, Inc.

Austin, TX • On-site

Part-time

Posted 4 days ago

Cadence Design Systems rating

9.0

Company rating: 9.0 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

33rd of 247 rated software companies

Job description

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Responsibilities
  • Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
  • Research and develop AI-driven approaches for geometry modeling, mesh generation, and topology optimization workflows.
  • Prototype and evaluate AI-assisted methods for automating geometry creation and simulation model preparation.
  • Work with researchers and engineers to integrate AI technologies into engineering and physics-based applications, including thermal and structural simulation.
  • Analyze experimental results and improve the quality, robustness, and performance of AI-generated geometry and mesh models.
  • Investigate methods to reduce manual modeling effort and accelerate design and simulation workflows through AI automation.
  • Contribute to technical discussions, documentation, research reports, and prototype software development.

Basic Qualifications
  • Currently pursuing a Master's degree or PhD in Computer Science, Engineering, Applied Mathematics, or a related field.
  • Strong foundation in data structures, algorithms, and software engineering principles.
  • Programming experience in C/C++ and Python.
  • Familiarity with software development practices, including debugging, testing, and version control.
  • Strong analytical, problem-solving, collaboration, and communication skills.
  • Curiosity and enthusiasm for applying AI technologies to engineering problems.

Preferred Qualifications
  • Experience with AI/ML, including deep learning, LLMs, GenAI, or GNNs.
  • Familiarity with geometric modeling, mesh generation, retopology, computational geometry, or graph-based representations.
  • Coursework or research experience in computer graphics, computer-aided engineering (CAE), scientific computing, or simulation.
  • Exposure to CAD, CAE, EDA, or simulation-driven design applications.
  • Interest in topology optimization, geometry processing, performance optimization, parallel computing, or GPU acceleration.
  • Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar tools.
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