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Edge Application Developer

The Select Group

Bellevue, Washington • Today

Full-time

Application Developer

Purdue University

Remote or Indiana • Today

Full-time

USD 62,400.00 - 66,300.00 per year

Teamcenter Application Developer

Accelint

Remote or Saint Paul, Minnesota • Today

Full-time

USD 125,506.00 - 148,769.00 per year

Enterprise Applications Development Manager

McKinstry Company

Seattle, Washington • Today

Full-time

USD 110,790.00 - 190,700.00 per year

Electronics Application Development Engineer

Nordson Corporation

Remote or California • Today

Full-time

USD 101,700.00 - 152,600.00 per year

Enterprise Application Engineer

University of Alaska

Remote or Juneau, Alaska • Today

Full-time

Applications Developer

A2 Global Electronics and Solutions

Remote • Yesterday

Easy Apply

Full-time

115,000 - 135,000

Application Engineer

JBT Marel Corporation

Remote or Ohio • Today

Full-time

USD 80,000.00 - 110,000.00 per year

Job Details

Skills

Science
Research
Dependability
Python
TensorFlow
Computer Vision
Deep Learning
Robotics
Communication
CUDA
Optimization
Fusion
Estimating
3D Computer Graphics
C++
Machine Learning (ML)
Computer Hardware
Telematics
Real-time
Collaboration
Embedded Systems
Sensors
Systems Architecture

Summary

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

76152

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10472636
Position Id: 76152
Posted 1 day ago

Fitment

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About Overland AI Founded in 2022 and headquartered in Seattle, Washington, Overland AI is transforming land operations for modern defense. The company leverages over a decade of advanced research in robotics and machine learning, as well as a field-test forward ethos, to deliver combined capabilities for unit commanders. Our OverDrive autonomy stack enables ground vehicles to navigate and operate off-road in any terrain without GPS or direct operator control. Our intuitive OverWatch C2 interfac

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On our Behaviors team (Prediction and ML Planner), you will have the opportunity to work with world-class ML engineers, whose mission is to make self-driving vehicles a reality and to create a positive social impact. The Behaviors team develops ML models that learn how different agents navigate complex real-world traffic scenarios. We leverage these models to set the behavior of our self-driving vehicle, and predict the behavior of other agents such as vehicles and vulnerable road users. We are

Full-time

USD 240,000.00 - 330,000.00 per year

Job Details

Skills

Science
Research
Dependability
Python
TensorFlow
Computer Vision
Deep Learning
Robotics
Communication
CUDA
Optimization
Fusion
Estimating
3D Computer Graphics
C++
Machine Learning (ML)
Computer Hardware
Telematics
Real-time
Collaboration
Embedded Systems
Sensors
Systems Architecture

Summary

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.

Dice Id: 10472636
Position Id: 76152
Posted 1 day ago

Fitment

Dice Job Match Score™

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Similar Jobs

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Staff Machine Learning Engineer

Remote or California

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Principal Engineer

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