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Nerf Machine Learning Jobs in California (NOW HIRING)

As a 3D Machine Learning Engineer , you will focus on designing, implementing, training, and ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...

You are familiar with the internals of modern machine learning (diffusion models, vision foundational models) or neural rendering (NeRF, 3DGS) systems beyond just application. You understand the ...

You are familiar with the internals of modern machine learning (diffusion models, vision foundational models) or neural rendering (NeRF, 3DGS) systems beyond just application. You understand the ...

You are familiar with the internals of modern machine learning (diffusion models, vision foundational models) or neural rendering (NeRF, 3DGS) systems beyond just application. You understand the ...

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Nerf Machine Learning information

What are the key skills and qualifications needed to thrive as a NeRF (Neural Radiance Fields) Machine Learning Engineer, and why are they important?

To thrive as a NeRF Machine Learning Engineer, you need a strong background in computer vision, deep learning, and mathematics, typically supported by a degree in computer science or a related field. Proficiency with Python, PyTorch or TensorFlow, 3D graphics libraries, and familiarity with NeRF-specific frameworks is essential. Strong problem-solving skills, creativity, and effective communication set standout engineers apart in this field. These skills enable the development of advanced 3D scene reconstruction models and ensure efficient collaboration within multidisciplinary teams.

How does a Nerf Machine Learning Engineer typically collaborate with 3D artists and graphics engineers in a project?

As a Nerf Machine Learning Engineer, you’ll frequently work alongside 3D artists and graphics engineers to integrate neural radiance field (NeRF) models into real-time rendering pipelines. Collaboration often involves translating real-world scene data processed by NeRF into formats that can be manipulated by artists, as well as optimizing model performance for interactive applications. Regular meetings and iterative feedback ensure that visual quality and technical requirements align, making strong communication and flexibility essential for success in this role.

What are Nerf Machine Learning jobs?

Nerf Machine Learning jobs involve working with Neural Radiance Fields (NeRF), a type of machine learning model used for 3D scene reconstruction from 2D images. Professionals in this field develop, train, and optimize NeRF algorithms to create realistic 3D representations for applications in computer vision, graphics, virtual reality, and robotics. These roles typically require strong backgrounds in deep learning, computer vision, and software engineering, along with experience in frameworks like PyTorch or TensorFlow.

What is the difference between Nerf Machine Learning vs Computer Vision Engineer?

AspectNerf Machine LearningComputer Vision Engineer
Required CredentialsDegree in Computer Science, Data Science, or related fields; experience with machine learning frameworksDegree in Computer Science, Electrical Engineering, or related fields; experience with image processing and vision algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural rendering and 3D modelingTech companies, research institutions, industries involving image analysis and autonomous systems
Industry UsagePrimarily in AI research, neural rendering, 3D scene reconstructionIn autonomous vehicles, robotics, healthcare imaging, and security systems

While both roles involve advanced AI techniques, Nerf Machine Learning focuses on neural radiance fields and 3D scene understanding, whereas Computer Vision Engineers specialize in analyzing and interpreting visual data from images and videos. The roles often overlap in AI research but serve different application areas within the tech industry.

What are popular job titles related to Nerf Machine Learning jobs in California? For Nerf Machine Learning jobs in California, the most frequently searched job titles are:
What cities in California are hiring for Nerf Machine Learning jobs? Cities in California with the most Nerf Machine Learning job openings:

3D Machine Learning Engineer

FieldAI

Irvine, CA • On-site

Full-time

Posted 18 days ago


Job description

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
As a 3D Machine Learning Engineer, you will focus on designing, implementing, training, and maintaining cutting-edge 3D and multimodal machine learning models that process reality capture data such as 3D point clouds, 360 photos, and RGBD images. Your work will directly contribute to automated progress tracking, deviation analysis, and semantic scene understanding of construction sites. You will collaborate closely with software, autonomy, and product teams to ensure seamless integration of these AI models into our production environments.
What You'll Do
  • 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.

What You Have
  • 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.

The Extras That Set You Apart
  • 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.

Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.
Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.
Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.
We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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.