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Geometric Deep Learning Jobs (NOW HIRING)

Preferred : • Experience working with BIM data, digital twins, or construction-related sensor data. • Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene ...

Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM.

Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations. * Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow. * Strong ...

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations. * Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow. * Strong ...

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations. * Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow. * Strong ...

Senior ML Engineer

$180K - $200K/yr

You'll work across optimization, machine learning, and geometric deep learning on a hard, real-world combinatorial problem. This is a fully distributed team. We expect high autonomy and high ...

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Geometric Deep Learning information

See salary details

$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.

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:

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.

Agentic AI & Graph Machine Learning Research Engineer

HRL Laboratories

Calabasas, CA

$140K - $175K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 7 days ago


Job description

HRL Laboratories, an IBM company, pioneers the next frontiers of physical and information science. Delivering transformative technologies in automotive, aerospace and defense, HRL advances the critical missions of its customers to help them remove limitations and create competitive advantage. HRL then transitions the work back to customers – ready for real-world application. For more than 70 years, HRL’s rich portfolio of scientific discoveries and engineering innovations continues to build on each other — often in unexpected, profound and far-reaching ways. As a private company owned by IBM, HRL prioritizes purpose over profit, significantly advancing the state of the art.

HRL Laboratories develops robust intelligent systems that deliver adaptable, autonomous performance improvement solutions for complex missions. Our teams advance human-machine synergy, operationalized machine learning models and complex systems analytics and agents to create scalable, secure technologies. We design novel algorithms and mission-ready solutions that strengthen decision making for autonomous and human-guided systems across national security and commercial applications.

Position Summary:

•    Lead and conduct research in agentic AI, intelligent decision support, autonomous workflows, and LLM-powered agent architectures integrating memory, planning, tool use, and retrieval
•    Design, develop, and evaluate multi-agent systems for distributed decision-making, coordination, communication, and long‑horizon task execution across mission-critical domains and applications
•    Build knowledge-enhanced AI systems that integrate structured knowledge sources, including knowledge graphs, GraphRAG pipelines, ontologies, and multimodal retrieval systems to improve reasoning and context awareness
•    Develop and apply graph machine learning (GML) and graph representation learning techniques (e.g., GNNs, geometric deep learning) to support pattern discovery, anomaly detection, and predictive analytics
•    Develop trustworthy AI systems, including Explainable AI (XAI), Verification & Validation (V&V), robustness testing, uncertainty quantification, and safety assessments for agentic and graph-based AI systems
•    Collaborate with multidisciplinary teams, publish high‑quality research, support proposal development, and engage with internal and external stakeholders


Required Qualifications:

•    Minimum: M.S. in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Network Science, or a related technical field plus 3+ years of relevant industry or research experience in AI/ML
•    Strong background in machine learning, deep learning, natural language processing, generative AI, and multimodal foundation models
•    Experience adapting and optimizing foundation models through prompt engineering, supervised fine tuning, parameter efficient fine tuning, preference optimization, model alignment, and inference optimization techniques
•    Experience developing LLM powered and agentic AI systems using modern agent frameworks (e.g., LangGraph, AutoGen, or equivalent)
•    Familiarity with AI interoperability standards and distributed agent architectures, including Model Context Protocol (MCP), Agent2Agent (A2A), or comparable frameworks for tool integration and multi agent communication
•    Hands on experience with graph mining, graph matching, geometric deep learning, and applied GML workflows
•    Experience with knowledge graphs, ontologies, graph schemas (e.g., LPG, RDF), graph databases (e.g., Neo4j), and graph query languages (e.g., Cypher)
•    Proficiency in Python, PyTorch, and modern software engineering practices (version control, testing, collaborative development)
•    Experience with large scale data processing and distributed systems (e.g., Ray, Spark), and optionally real time streaming or online learning pipelines
•    Experience deploying scalable AI systems using modern LLMOps/AgentOps, distributed inference, GPU acceleration, model serving frameworks (e.g., vLLM, SGLang), observability, and cloud native infrastructure

Preferred Qualifications:

•    Ph.D. in a relevant technical discipline with research experience in agentic AI, foundation models, graph machine learning, geometric deep learning, autonomous systems, or related areas
•    Prior research publications in top tier AI/ML venues (e.g., NeurIPS, ICML, ICLR, KDD, WWW, AAAI) are highly desirable

Special Requirements:

•    U.S. Citizenship with the ability to obtain and maintain a U.S. Government Security Clearance

Compensation and Benefits:

•    Pay Range: $140,700 - $175,950 
•    Our salary ranges are determined by role, level, and location (California). The range
displayed on each job posting reflects the target range for new hire salaries for the position.
Within the range, individual pay is determined by work location and additional factors,
including job-related skills, experience, and relevant education or training. Your recruiter
can share more about the specific salary range during the hiring process.
•     Benefits: HRL offers a generous and very competitive total compensation and benefits
package. Our Regular/Full Time benefits include medical, dental, vision, life insurance,
401K match, gym facilities, PTO, Sick time, upward mobility, and an exciting and
challenging work environment.

•    For more information about our company benefit offerings please visit:
https://www.hrl.com/careers/benefits

Non-Discrimination and Equal Employment Opportunities (U.S.)
Don’t meet every single requirement? Studies have shown that some people are less likely to apply
to jobs unless they meet every single desired qualification. At HRL, we are dedicated to building a
diverse, inclusive, and authentic workplace, so if you’re excited about this role but your past
experience doesn’t align perfectly with every qualification in the job description, we encourage you
to apply anyway. You may be just the right candidate for this or other roles.
We are proud to be an EEO/AA employer M/F/D/V. We maintain a drug-free workplace and perform
pre-employment substance abuse testing.

If you would like more information about Equal Employment Opportunity as an applicant under the
law, please go to Employees & Job Applicants \u007C U.S. Equal Employment Opportunity Commission
For our privacy policy please visit: www.hrl.com/privacy

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.