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 ...
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 ...
Senior Scientist, Machine Learning (Biologics Design)
Foster City, CA · On-site
$169K - $219K/yr
Apply and extend modern deep learning approachesrelevant to biologics, including protein language models,geometric deep learning, and generative methods (e.g., diffusion, inverse folding,ProteinMPNN ...
Senior Scientist, Machine Learning (Biologics Design)
Foster City, CA · On-site
$169K - $219K/yr
Apply and extend modern deep learning approachesrelevant to biologics, including protein language models,geometric deep learning, and generative methods (e.g., diffusion, inverse folding,ProteinMPNN ...
Apply and extend modern deep learning approachesrelevant to biologics, including protein language models,geometric deep learning, and generative methods (e.g., diffusion, inverse folding,ProteinMPNN ...
Apply and extend modern deep learning approachesrelevant to biologics, including protein language models,geometric deep learning, and generative methods (e.g., diffusion, inverse folding,ProteinMPNN ...
Senior Scientist, Machine Learning (Biologics Design)
Foster City, CA · On-site
$169K - $219K/yr
Apply and extend modern deep learning approaches relevant to biologics, including protein language models, geometric deep learning, and generative methods (e.g., diffusion, inverse folding ...
Senior Scientist, Machine Learning (Biologics Design)
Foster City, CA · On-site
$169K - $219K/yr
Apply and extend modern deep learning approaches relevant to biologics, including protein language models, geometric deep learning, and generative methods (e.g., diffusion, inverse folding ...
3D Machine Learning Engineer
Irvine, CA · On-site
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
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
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 ...
Quick apply
3D Machine Learning Engineer
Irvine, CA · On-site
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
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
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 ...
Machine Learning Research Scientist/Senior Machine Learning Research Scientist, Structure and Sim...
$168K - $312K/yr
Demonstrated experience architecting and training deep learning models, particularly utilizing modern approaches (e.g., multimodal representation learning, geometric deep learning, and diffusion ...
Machine Learning Research Scientist/Senior Machine Learning Research Scientist, Structure and Sim...
$168K - $312K/yr
Demonstrated experience architecting and training deep learning models, particularly utilizing modern approaches (e.g., multimodal representation learning, geometric deep learning, and diffusion ...
Background in differentiable optimization and geometric deep learning approaches. Experience optimizing ML models for mobile deployment and resource-constrained environments. Knowledge of SLAM ...
Background in differentiable optimization and geometric deep learning approaches. Experience optimizing ML models for mobile deployment and resource-constrained environments. Knowledge of SLAM ...
Background in differentiable optimization and geometric deep learning approaches. Experience optimizing ML models for mobile deployment and resource-constrained environments. Knowledge of SLAM ...
Background in differentiable optimization and geometric deep learning approaches. Experience optimizing ML models for mobile deployment and resource-constrained environments. Knowledge of SLAM ...
Autonomy Engineer - Deep Learning
San Mateo, CA · On-site
$170K - $277K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Autonomy Engineer - Deep Learning
San Mateo, CA · On-site
$170K - $277K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property ...
Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property ...
Senior Autonomy Engineer - Deep Learning
San Mateo, CA · On-site
$170K - $277K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Senior Autonomy Engineer - Deep Learning
San Mateo, CA · On-site
$170K - $277K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Autonomy Engineer - Deep Learning Infrastructure
San Mateo, CA · On-site
$170K - $236K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Autonomy Engineer - Deep Learning Infrastructure
San Mateo, CA · On-site
$170K - $236K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Machine Learning Engineer / Researcher
San Mateo, CA · On-site +1
$110K - $175K/yr
Stay current on relevant ML research (sequence models, geometric deep learning, representation learning) and apply pragmatically Requirements * 4+ years of hands-on ML experience, or equivalent ...
Quick apply
Machine Learning Engineer / Researcher
San Mateo, CA · On-site +1
$110K - $175K/yr
Stay current on relevant ML research (sequence models, geometric deep learning, representation learning) and apply pragmatically Requirements * 4+ years of hands-on ML experience, or equivalent ...
CVML Engineer - 3D Reconstruction
$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 ...
CVML Engineer - 3D Reconstruction
$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 ...
Autonomy Engineer - Deep Learning (San Mateo)
San Mateo, CA · On-site
$170K - $277K/yr
About the role Learning a semantic and geometric understanding of the world from visual data is the ... As a Deep Learning Engineer, you will be responsible for training and deploying optimized models to ...
Autonomy Engineer - Deep Learning (San Mateo)
San Mateo, CA · On-site
$170K - $277K/yr
About the role Learning a semantic and geometric understanding of the world from visual data is the ... As a Deep Learning Engineer, you will be responsible for training and deploying optimized models to ...
Autonomy Engineer - Deep Learning Model Acceleration
San Mateo, CA · On-site
$170K - $277K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
Autonomy Engineer - Deep Learning Model Acceleration
San Mateo, CA · On-site
$170K - $277K/yr
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to ...
CVML Engineer - 3D Reconstruction
$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 ...
CVML Engineer - 3D Reconstruction
$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 ...
Senior Software Engineer - cuEquivariance
Santa Clara, CA · On-site
$143K - $189K/yr
PhD or equivalent experience in computational chemistry, biophysics, physics, or computer science with a focus on geometric deep learning or HPC. * Contributions to open-source geometric ML or GPU ...
Senior Software Engineer - cuEquivariance
Santa Clara, CA · On-site
$143K - $189K/yr
PhD or equivalent experience in computational chemistry, biophysics, physics, or computer science with a focus on geometric deep learning or HPC. * Contributions to open-source geometric ML or GPU ...
Geometric Deep Learning information
What is geometric deep learning?
Geometric deep learning is a branch of machine learning focused on designing neural networks that operate on non-Euclidean data such as graphs and manifolds. It involves techniques like graph neural networks and requires understanding of both deep learning and geometric structures, often using tools like PyTorch or TensorFlow. Professionals in this field develop models for applications like social network analysis, 3D shape recognition, and molecular modeling.
What is the difference between Geometric Deep Learning vs Data Scientist?
| Aspect | Geometric Deep Learning | Data Scientist |
|---|---|---|
| Required Credentials | Advanced degrees in computer science, machine learning, or related fields | Bachelor's or master's in data science, statistics, or related fields |
| Work Environment | Research labs, AI development teams, academia | Business analytics, product teams, consulting firms |
| Industry Usage | AI, robotics, computer vision, graph analysis | Business 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.
Which 5 jobs will survive AI?
Geometric Deep Learning specialists are likely to continue in demand due to their expertise in advanced neural network architectures and 3D data processing. Jobs involving complex problem-solving, creativity, and domain-specific knowledge—such as data scientists, AI researchers, software engineers, cybersecurity analysts, and healthcare professionals—are expected to persist as AI tools augment rather than replace these roles. Continuous learning and proficiency with AI frameworks like TensorFlow or PyTorch enhance job security in these fields.
What engineer makes $500,000 a year?
Senior engineers in specialized fields such as software engineering, data engineering, or machine learning engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries like technology or finance. These roles often require expertise in programming, system design, and sometimes leadership or management responsibilities.
What are popular job titles related to Geometric Deep Learning jobs in California? For Geometric Deep Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Geometric Deep Learning jobs in California look for? The top searched job categories for Geometric Deep Learning jobs in California are:
What cities in California are hiring for Geometric Deep Learning jobs? Cities in California with the most Geometric Deep Learning job openings:
Full-time
Posted 24 days ago
Job description
Job Summary:
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
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 representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
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 representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.