Why This Role Stands Out
This hybrid Edge Application Developer role offers a unique opportunity to bridge cutting-edge machine learning research with real-world, driver-facing features, providing significant growth in deploying and optimizing models on edge devices. You'll thrive if you possess strong Python skills and experience with edge inference frameworks, contributing to innovative automotive technology within a collaborative environment.
Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Santa Clara, CA, United States
Posted
3 weeks ago
Machine LearningRoboticsCUDAComputer VisionC++PythonTensorFlow
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
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
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.
- 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.
- 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
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