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3D Machine Learning Jobs in Everett, WA (NOW HIRING)

Amazon

Applied Science Manager, Prime Air

Seattle, WA · On-site

You will bridge the gap between Geospatial Intelligence and Machine Learning to revolutionize our ... Utilize deep geospatial data (3D maps, urban topology, etc.) to improve situational awareness and ...

Amazon

Applied Science Manager, Prime Air

Seattle, WA · On-site

You will bridge the gap between Geospatial Intelligence and Machine Learning to revolutionize our ... Utilize deep geospatial data (3D maps, urban topology, etc.) to improve situational awareness and ...

Meta

Surfacing Designer

Seattle, WA

$118K/yr

Experience utilizing rapid prototyping techniques such as 3D printing and CNC machining to help ... Please note that Meta may leverage artificial intelligence and machine learning technologies in ...

Adobe

Sr Applied Scientist, Generative AI/ML

Seattle, WA

$142K - $270K/yr

... Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing. The related applications include image/video/3D generation, editing, and understanding, conditioned on controls ...

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All Jobs3D Machine Learning Jobs3D Machine Learning Jobs in Everett, WA

3D Machine Learning information

See Everett, WA salary details

$28.2K

$47K

$97.2K

How much do 3d machine learning jobs pay per year?

As of Aug 18, 2026, the average yearly pay for 3d machine learning in Everett, WA is $47,041.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,900.00 and $50,800.00 per year, depending on experience, location, and employer.

What is 3d machine learning?

3D machine learning is a field of artificial intelligence focused on developing algorithms and models that can process and understand three-dimensional data. This includes tasks such as object recognition, scene reconstruction, segmentation, and analysis using 3D data formats like point clouds, meshes, or volumetric grids. Applications of 3D machine learning are found in areas like autonomous driving, robotics, medical imaging, and augmented reality. The field combines techniques from computer vision, deep learning, and geometry processing to interpret complex spatial information.

What are the key skills and qualifications needed to thrive as a 3d machine learning engineer, and why are they important?

To thrive as a 3D Machine Learning Engineer, you need a solid background in computer science, mathematics, and experience with 3D data processing and machine learning algorithms, typically supported by a relevant degree. Expertise in tools and frameworks like Python, PyTorch or TensorFlow, and libraries such as Open3D or PCL is commonly required, along with familiarity with 3D data formats. Strong problem-solving skills, creativity, and effective communication set top performers apart in this role. These skills enable the development of innovative solutions for complex 3D data challenges, which are crucial for advancements in fields like robotics, computer vision, and AR/VR.

What are some common challenges faced by professionals working in 3d machine learning, and how can they be addressed?

Professionals in 3D machine learning often encounter challenges such as handling large and complex datasets, managing high computational requirements, and ensuring model robustness across diverse 3D data types (e.g., point clouds, meshes, voxel grids). Addressing these challenges typically involves using efficient data preprocessing pipelines, leveraging cloud computing or advanced GPU resources, and staying updated with the latest research on 3D data augmentation and model architectures. Collaboration with multidisciplinary teams—including data engineers, computer vision experts, and domain specialists—is also crucial for overcoming technical obstacles and producing practical, scalable solutions.

What is the difference between 3D Machine Learning vs 3D Computer Vision?

Aspect3D Machine Learning3D Computer Vision
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegree in Computer Vision, Computer Science, or related fields; experience with image processing
Work EnvironmentResearch labs, AI development teams, tech companiesImaging labs, robotics, autonomous vehicles, tech firms
Industry UsageDeveloping models for 3D data analysis, sensor data integrationProcessing 3D images, object detection, scene reconstruction

While 3D Machine Learning focuses on creating algorithms that learn from 3D data, 3D Computer Vision emphasizes interpreting and analyzing 3D visual information. Both fields often overlap but serve different primary objectives within AI and imaging applications.

What are popular job titles related to 3D Machine Learning jobs in Everett, WA?

For 3D Machine Learning jobs in Everett, WA, the most frequently searched job titles are:

What job categories do people searching 3D Machine Learning jobs in Everett, WA look for?

The top searched job categories for 3D Machine Learning jobs in Everett, WA are:

What cities near Everett, WA are hiring for 3D Machine Learning jobs?

Cities near Everett, WA with the most 3D Machine Learning job openings:

3D Machine Learning jobs near you
Infographic showing various 3D Machine Learning job openings in Everett, WA as of July 2026, with employment types broken down into 86% Full Time, 10% Part Time, and 4% Contract. Highlights an 90% Physical, 3% Hybrid, and 7% Remote job distribution, with an average salary of $47,041 per year, or $22.6 per hour.

Edge Application Developer

The Select Group

Bellevue, WA • On-site

Full-time

Posted 10 days ago

Job description

Edge ML Application Developer- Bellevue, WA or Santa Clara, CA
This engagement focuses on building the critical integration layer between the client's on-vehicle machine learning models and real-time, driver-facing features on an edge-compute platform. The internal science team owns core model development; our role is to partner with them to deploy, optimize, and productionize those models on-device; translating research-grade models into reliable, real-time application logic. This includes preparing and synchronizing sensor inputs, tuning models for on-device performance constraints, and architecting the logic that arbitrates and combines outputs from multiple concurrent models into a single, dependable feature decision the driver can trust.
What You'll Bring
  • Strong Python experience with hands-on deployment and optimization of machine learning models on edge or embedded devices.
  • Experience with edge inference frameworks such as TensorRT, ONNX Runtime, TensorFlow Lite, or comparable technologies.
  • Experience deploying production computer vision, perception, or deep-learning models in real-time or near-real-time environments.
  • Prior experience within automotive, ADAS, autonomous vehicles, robotics, drones, or another sensor-driven autonomous system.
  • Experience working with camera, LiDAR, radar, IMU, or other sensor-based perception inputs.
  • Experience integrating outputs from multiple models or perception components into unified application or decision logic.
  • Strong understanding of latency, memory, throughput, and compute constraints in edge environments.
  • Experience with preprocessing, post-processing, confidence thresholds, filtering, tracking, fusion, or output arbitration.
  • Ability to work North American business hours with strong written and verbal communication skills.

Bonus Experience
  • Experience with NVIDIA Jetson, Orin, DRIVE, CUDA, or DeepStream.
  • Experience with model quantization and optimization techniques such as INT8, FP16, pruning, distillation, or layer fusion.
  • Experience with ADAS, collision avoidance, driver monitoring, or other safety-critical vehicle systems.
  • Experience with sensor fusion, multi-camera perception, LiDAR processing, trajectory estimation, or 3D perception.
  • Strong C++ experience for performance-sensitive inference or perception applications.

What You'll Do
  • Port, compile, and deploy ML models to resource-constrained edge-compute platforms.
  • Optimize model inference for latency, memory, throughput, and hardware constraints.
  • Build preprocessing pipelines for camera, telematics, and other sensor inputs.
  • Develop post-processing and application logic that combines outputs from multiple concurrent models into unified real-time decisions.
  • Implement confidence filtering, prioritization, and arbitration logic across competing model outputs and driver notifications.
  • Integrate perception outputs into real-time vehicle features such as alerts, visual indicators, or audible warnings.
  • Collaborate with perception, platform, embedded, and OS engineering teams to ensure sensor-data, timing, and runtime compatibility.
  • Execute against an established system architecture while iterating quickly as requirements and implementation details evolve.

TSG is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. #LI-CH1
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