> Markdown version of [/jobs/ext/2024450-edge-ml-application-developer](https://www.wearedevelopers.com/jobs/ext/2024450-edge-ml-application-developer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Edge ML Application Developer - **Company:** SELECT GROUP - **Location:** Bellevue, WA, United States - **Contract:** Permanent contract - **Skills:** Business Logic, Computing Platforms, Computer Vision, C++ (Programming Language), Nvidia CUDA, Data Transformation, Python (Programming Language), Machine Learning, Tensorflow, Sensor Fusion, Systems Architecture, Deep Learning, Low Latency, Optimization Algorithms, ONNX (Open Neural Network Exchange) Format, TensorRT, Lidar - **Published:** August 11, 2026 - **Apply:** https://www.dice.com/job-detail/c88fc641-751f-47e1-b3e7-03673a8c97ab ## About the Role * 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. ## Description 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., * 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. ## Related Videos - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [How to develop an autonomous car end-to-end: Robotic Drive and the mobility revolution](https://www.wearedevelopers.com/videos/22-how-to-develop-an-autonomous-car-end-to-end-robotic-drive-and-the-mobility-revolution) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [From Perception to Autonomy: Building Agentic Edge AI Robots with ROS 2](https://www.wearedevelopers.com/videos/100295-from-perception-to-autonomy-building-agentic-edge-ai-robots-with-ros-2) - [Remote Driving on Plant Grounds with State-of-the-Art Cloud Technologies](https://www.wearedevelopers.com/videos/251-remote-driving-on-plant-grounds-with-state-of-the-art-cloud-technologies) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)