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Geometric Deep Learning Jobs in California (NOW HIRING)

PlusAI

Computer Vision Engineer Intern

Santa Clara, CA · On-site

$19 - $65/hr

Ensure alignment and geometric consistency between different sensor modalities within the ... Deep understanding of machine learning principles and methodologies * Experience with implementing ...

PlusAI

Computer Vision Engineer Intern

Santa Clara, CA · On-site

$19 - $65/hr

Ensure alignment and geometric consistency between different sensor modalities within the ... Deep understanding of machine learning principles and methodologies * Experience with implementing ...

Amazon

Senior Applied Scientist

San Francisco, CA · On-site

$107K - $147K/yr

You will develop ML-based methods to extract semantic and geometric information from radar point ... You will lead research that translates cutting-edge advances in deep learning and computer vision ...

The Bot Company

3D Vision

San Francisco, CA · On-site

$200K - $350K/yr

Develop geometric vision pipelines-SLAM, reconstruction, tracking-and integrate them with learned models. * Implement and optimize deep learning models for depth, flow, correspondence, and 3D ...

Mach9 Robotics Inc

ML Engineer

San Francisco, CA · On-site

Strong foundation in computer vision and deep learning, with hands-on experience training models ... Working knowledge of geometric concepts relevant to 3D perception like coordinate systems and 3D ...

Amazon

Senior Applied Scientist

San Francisco, CA

$107K - $147K/yr

You will develop ML-based methods to extract semantic and geometric information from radar point ... in deep learning and computer vision to these underexplored but high-impact sensing modalities.

Amazon

Senior Applied Scientist

San Francisco, CA

$107K - $147K/yr

You will develop ML-based methods to extract semantic and geometric information from radar point ... in deep learning and computer vision to these underexplored but high-impact sensing modalities.

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All JobsGeometric Deep Learning JobsGeometric Deep Learning Jobs in California

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?

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.

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:
Geometric Deep Learning jobs near you
Infographic showing various Geometric Deep Learning job openings in California as of June 2026, with employment types broken down into 9% Internship, and 91% Full Time. Highlights an 96% In-person, and 4% Remote job distribution.
Computer Vision Engineer Intern

Computer Vision Engineer Intern

PlusAI

Santa Clara, CA • On-site

$19 - $65/hr

Full-time, Internship

Retirement

Posted 15 days ago

Job description

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World's Most Innovative Companies. Partners including TRATON GROUP's Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you're ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.
Construct Digital Twins: Develop and optimize pipelines to reconstruct high-fidelity, multi-modal 3D representations of key driving routes using recorded vehicle data (camera, LiDAR, IMU, and GPS).
Implement Next-Gen Tech: Apply state-of-the-art neural rendering and view-synthesis techniques, such as 3D Gaussian Splatting (3DGS) and NeRFs, to handle challenging real-world lighting, weather, and dynamic objects.
Drive Validation & Verification (V&V): Integrate reconstructed environments into our perception testing framework to stress-test object detection, tracking, and edge-case scenarios.
Evaluate Multi-Modal Accuracy: Ensure alignment and geometric consistency between different sensor modalities within the reconstructed digital twin.
Collaborate & Scale: Work closely with the Simulation and Perception teams to turn research prototypes into robust, scalable tooling that directly impacts our production deployment timeline.
Responsibilities:
  • Work on generating 3D/4D scene reconstructions to simulate driving scenes while generating realistic sensor data including multiple view camera and lidar

Required Skills:
  • Pursuing MS or PhD in CS, EE, mathematics, statistics or related field
  • Deep understanding of machine learning principles and methodologies
  • Experience with implementing deep learning models in at least one deep learning framework (PyTorch, Tensorflow, Jax)

Preferred Skills:
  • Past experiences in deep learning projects involving object detection, motion tracking or semantic segmentation
  • Experience with 3D Vision
  • Publication record in relevant venues (CVPR, ICLR, ICCV, ECCV, NeurIPS, AAAI, SIGGRAPH)

$19 - $65 an hour
Our internship hourly rates are a standard pay determined based on the position and your location, year in school, degree, and experience.
Your opportunities joining PlusAI
Work, learn and grow in a highly future-oriented, innovative and dynamic field.
Wide range of opportunities for personal and professional development.
Catered free lunch, unlimited snacks and beverages.
Highly competitive salary and benefits package, including 401(k) plan.
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.

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