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Geometric Deep Learning Jobs in Texas (NOW HIRING)

Koch Industries

Machine Learning Engineer

Austin, TX · On-site

$170K - $250K/yr

Geometric deep learning (graph neural networks, mesh-based models) for CAD data. * Background in RF/high-speed electronics or interconnect design. For this role, we anticipate paying $170,000 - $250 ...

CACI

Machine Learning Engineer

San Antonio, TX · Hybrid

Deep knowledge of ML frameworks such as PyTorch or TensorFlow. Programming: Strong Python. ML ... Geometric, MLflow. Explainability tools such as SHAP, GNNExplainer, or attention-based ...

Cadence

Experience with AI/ML, including deep learning, LLMs, GenAI, or GNNs. * Familiarity with geometric modeling, mesh generation, retopology, computational geometry, or graph-based representations.

New

MD Anderson

Postdoctoral Fellow - Genomic Medicine

Houston, TX · On-site +1

$46K - $63K/yr

Expertise with PyTorch and torch-geometric, or equivalent technologies, is essential. Experience with other deep learning and generative methods is preferred. Experience working in a high-performance ...

Cisco

Applied AI Scientist

Houston, TX · On-site

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 ...

Cisco

Applied AI Scientist

Austin, TX · On-site

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 ...

Cisco

Applied AI Scientist

Dallas, 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

Pearland, 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

San Marcos, 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

Grand Prairie, 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 ...

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All JobsGeometric Deep Learning JobsGeometric Deep Learning Jobs in Texas

Geometric Deep Learning information

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 Texas?

For Geometric Deep Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Geometric Deep Learning jobs in Texas look for?

The top searched job categories for Geometric Deep Learning jobs in Texas are:

What cities in Texas are hiring for Geometric Deep Learning jobs?

Cities in Texas with the most Geometric Deep Learning job openings:

Geometric Deep Learning jobs near you
Infographic showing various Geometric Deep Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer

Koch Industries

Austin, TX • On-site

$170K - $250K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired 1 day ago. Applications are no longer accepted.

Key responsibilities

  • Build physics-informed surrogate models on Azure Machine Learning to predict engineering simulation outcomes from design parameters.

  • Incorporate physics-informed constraints and build model-uncertainty and confidence scoring to optimize simulation validation processes.

  • Deploy, version, and monitor models via Azure ML endpoints and model registry, and benchmark surrogate models against full-simulation speed.

Koch Industries rating

8.0

Company rating: 8.0 out of 10

Based on 53 frontline employees who took The Breakroom Quiz

138th of 545 rated manufacturers

Job description

Your Job
The ML Engineer will build physics-informed surrogate models on Azure Machine Learning that predict engineering simulation outcomes directly from design parameters. They will be pre-screening candidate designs in milliseconds so only the most promising ones require full high-fidelity simulation, accelerating the design-optimization cycle.
Our Team
Established in 1938, Molex delivers comprehensive electronic solutions for various markets, including data communications, telecommunications, consumer electronics, industrial, automotive, commercial vehicle, aerospace and defense, medical, and lighting. You'll join the platform team behind our Azure AI/ML engineering tools, partnering closely with data scientists, LLM engineers, and MLOps teams to keep GPU-heavy training and simulation workloads reliable and fast.
What You Will Do
  • Design and train surrogate models (neural networks, Gaussian processes, gradient-boosted trees, GNNs/PINNs) on Azure GPU compute (ND/NC series).
  • Incorporate physics-informed constraints so predictions stay physically valid, not just statistically fit.
  • Build model-uncertainty and confidence scoring to decide which designs need full simulation validation, then retrain as new results arrive.
  • Deploy and version models via Azure ML endpoints and model registry; monitor for drift on a rolling basis.
  • Benchmark surrogate vs. full-simulation speedup to guide platform-level performance tuning.

Who You Are (Basic Qualifications)
  • Extensive hands-on experience building, training, and deploying ML models in production - not just using pretrained APIs.
  • 10+ years building ML for physical/engineering systems (surrogate modeling, physics-informed ML, or scientific ML).
  • Strong Python with PyTorch or TensorFlow.
  • Understanding of relevant engineering/physics fundamentals and simulation data formats for your domain.
  • Experience with Azure Machine Learning or a similar cloud ML platform.
  • Familiarity with uncertainty quantification (Bayesian approaches, ensembling).

What Will Put You Ahead
  • Direct experience with industry-standard EM or physics simulation tools.
  • Geometric deep learning (graph neural networks, mesh-based models) for CAD data.
  • Background in RF/high-speed electronics or interconnect design.

For this role, we anticipate paying $170,000 - $250,000 per year. This role is eligible for variable pay, issued as a monetary bonus or in another form.
At Koch companies, we are entrepreneurs. This means we openly challenge the status quo, find new ways to create value and get rewarded for our individual contributions. Any compensation range provided for a role is an estimate determined by available market data. The actual amount may be higher or lower than the range provided considering each candidate's knowledge, skills, abilities, and geographic location. If you have questions, please speak to your recruiter about the flexibility and detail of our compensation philosophy.
Hiring Philosophy
All Koch companies value diversity of thought, perspectives, aptitudes, experiences, and backgrounds. We are Military Ready and Second Chance employers. Learn more about our hiring philosophy here .
Who We Are
As a Koch company, Molex is a leading supplier of connectors and interconnect components, driving innovation in electronics and supporting industries from automotive to health care and consumer to data communications. The thousands of innovators who work for Molex have made us a global electronics leader. Our experienced people, groundbreaking products and leading-edge technologies help us deliver a wider array of solutions to more markets than ever before.
At Koch, employees are empowered to do what they do best to make life better. Learn how our business philosophy helps employees unleash their potential while creating value for themselves and the company.
Our Benefits
Our goal is for each employee, and their families, to live fulfilling and healthy lives. We provide essential resources and support to build and maintain physical, financial, and emotional strength - focusing on overall wellbeing so you can focus on what matters most. Our benefits plan includes - medical, dental, vision, flexible spending and health savings accounts, life insurance, ADD, disability, retirement, paid vacation/time off, educational assistance, and may also include infertility assistance, paid parental leave and adoption assistance. Specific eligibility criteria is set by the applicable Summary Plan Description, policy or guideline and benefits may vary by geographic region. If you have questions on what benefits apply to you, please speak to your recruiter.
Additionally, everyone has individual work and personal needs. We seek to enable the best work environment that helps you and the business work together to produce superior results.
Equal Opportunities
Equal Opportunity Employer, including disability and protected veteran status. Except where prohibited by state law, some offers of employment are conditioned upon successfully passing a drug test. This employer uses E-Verify. Please click here for additional information. (For Illinois E-Verify information click here , aquí , or tu ).

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