Edge Computing Engineer
Weekly Rate: $8,500/week
Overview
The Edge Computing Engineer specializes in deploying and optimizing the system on edge hardware platforms. This role is crucial for meeting the sub-200ms latency requirement while managing resource constraints on devices like NVIDIA Jetson, ensuring real-time performance in production environments.
Key Responsibilities
NVIDIA Jetson Platform Optimization - Deploy and optimize the squat tracking system on NVIDIA Jetson platforms, configuring hardware for maximum performance. Build efficient DeepStream pipelines that process multiple video streams while maintaining sub-200ms latency requirements.
Hardware Acceleration Implementation - Leverage GPU, DLA (Deep Learning Accelerator), and VPU (Vision Processing Unit) capabilities to maximize inference performance. Implement optimized CUDA kernels and utilize TensorRT for model acceleration on edge devices.
Resource Management and Optimization - Manage memory, power, and thermal constraints effectively on edge hardware. Implement strategies that keep memory usage under 4GB per stream and power consumption below 30W while preventing thermal throttling.
Multi-Stream Video Processing - Design systems capable of handling multiple camera feeds simultaneously with consistent performance. Implement efficient resource allocation and scheduling to process 60fps video from multiple angles without frame drops.
Real-Time Performance Guarantees - Configure real-time operating systems and optimize kernel parameters for deterministic performance. Ensure consistent sub-200ms latency through careful system tuning and priority management.
Edge-to-Cloud Architecture - Design efficient communication protocols between edge devices and cloud services. Implement smart caching strategies and data synchronization that minimizes bandwidth usage while ensuring data integrity.
System Monitoring and Telemetry - Develop comprehensive monitoring solutions that track edge device health, performance metrics, and resource utilization. Implement alerting systems for proactive issue detection and resolution.
Thermal and Power Management - Design and implement thermal management solutions including active cooling strategies and dynamic frequency scaling. Optimize power consumption profiles for different operational modes.
Network Optimization - Minimize communication delays through network optimization, implementing efficient protocols for low-latency data transmission. Design systems that maintain performance even with variable network conditions.
Fleet Management and Deployment - Develop tools and procedures for managing fleets of edge devices across multiple venues. Implement remote update capabilities, configuration management, and automated deployment workflows.
Required Skills
Embedded Systems Excellence demonstrates strong experience optimizing performance on resource-constrained hardware where efficient resource utilization is critical for system success. They have solved challenging problems with thermal management, power budgets, and memory limitations that can impact system stability under load. Their experience includes developing creative solutions when standard approaches are insufficient due to unchangeable hardware constraints.
NVIDIA Jetson Platform Expertise shows hands-on experience deploying complex computer vision systems on Jetson hardware with solid understanding of performance tuning methodologies. They have configured hardware accelerators and optimized resource allocation to achieve high throughput while maintaining power and thermal efficiency. Their C++ and CUDA programming skills enable development of custom optimizations when standard libraries cannot meet specific performance requirements.
Real-Time Systems and Hardware Acceleration involves practical experience with DeepStream SDK and real-time systems concepts needed to achieve consistent sub-200ms latency performance. They understand how to effectively balance GPU, DLA, and VPU resources for optimal performance across multiple concurrent video processing streams. Their expertise helps ensure predictable system behavior even under demanding operational conditions during live competitions.
Network Programming and System Integration combines networking knowledge with system-level programming skills that minimize communication latencies between edge devices and cloud infrastructure. They have implemented efficient communication protocols that maintain performance across varying network conditions during live event scenarios. Their system integration experience ensures smooth operation across the complete edge computing pipeline from camera input through final output delivery.
Phase Allocation
The Edge Computing Engineer begins with partial involvement during Alpha phase for initial hardware evaluation and architecture planning. Full-time engagement spans Beta through Delta phases, coinciding with intensive edge deployment development, optimization, and field testing. The role scales back during Full Release while maintaining support for production deployments and performance tuning.
| Phase | Weekly Rate | Allocation | Duration |
|---|---|---|---|
| Alpha | $4,250/week | 50% | 10 weeks |
| Beta | $8,500/week | 100% | 12 weeks |
| Gamma | $8,500/week | 100% | 8 weeks |
| Delta | $8,500/week | 100% | 10 weeks |
| Full Release | $4,250/week | 50% | 12 weeks |
Deliverables
Edge Deployment Configurations. Complete configuration packages for deploying computer vision algorithms on edge hardware, including optimized settings for different device models and deployment scenarios. These configurations ensure consistent performance across heterogeneous edge devices while maximizing resource utilization and maintaining system reliability.
DeepStream Pipeline Implementations. Production-ready NVIDIA DeepStream pipelines optimized for real-time video processing on Jetson platforms, including custom plugins and optimizations specific to squat tracking requirements. These implementations leverage hardware acceleration capabilities to achieve maximum throughput while maintaining the required sub-200ms latency targets.
Performance Optimization Reports. Comprehensive analysis of edge computing performance characteristics, including bottleneck identification, optimization strategies, and achieved improvements. These reports document the optimization journey from initial deployment to production-ready configurations, providing insights for future enhancements and hardware selections.
Resource Utilization Metrics. Detailed profiling of CPU, GPU, memory, and power consumption across different operational scenarios and hardware configurations. These metrics establish baseline performance characteristics and guide capacity planning decisions for large-scale deployments across multiple venues.
Thermal Management Solutions. Engineered cooling strategies and thermal throttling configurations that maintain optimal operating temperatures during extended competition events. These solutions balance performance requirements with hardware longevity, ensuring reliable operation in various environmental conditions encountered at competition venues.
Failover Procedures. Robust failover mechanisms and recovery procedures that ensure continuous operation during hardware failures or network disruptions. These procedures include automatic failover to backup devices, graceful degradation strategies, and rapid recovery protocols that minimize service interruption.
Fleet Management Tools. Centralized management solutions for monitoring, updating, and maintaining distributed edge devices across multiple venues. These tools enable remote diagnostics, coordinated software updates, and performance monitoring of the entire edge infrastructure from a single control point.
Deployment Documentation. Comprehensive guides covering edge device setup, configuration, troubleshooting, and maintenance procedures. This documentation enables field technicians to deploy and maintain edge infrastructure efficiently while providing clear escalation paths for complex issues.
Success Criteria
Latency Target Achievement. Edge computing infrastructure consistently delivers sub-200ms end-to-end processing latency from video frame capture to result generation. This performance is maintained under full competition load with multiple concurrent video streams processing simultaneously on each edge device.
Video Processing Performance. The system maintains 60 frames per second video processing throughput without frame drops or quality degradation. This ensures smooth motion tracking and accurate pose estimation while preserving the temporal resolution necessary for precise movement analysis.
Memory Efficiency Standards. Edge deployments operate within 4GB memory constraints per video stream, enabling multiple concurrent streams on standard edge hardware. Memory management strategies prevent memory leaks during extended operation and ensure stable performance throughout multi-day competition events.
Power Consumption Optimization. Edge devices maintain sub-30W power consumption during active processing, enabling deployment with standard venue power infrastructure. Power efficiency optimizations balance performance requirements with thermal constraints and operational costs for large-scale deployments.
Reliability and Uptime Requirements. Edge infrastructure achieves 99.9% uptime across all deployed devices, with robust error recovery and automatic healing capabilities. This reliability standard ensures consistent service delivery during critical competition periods with minimal manual intervention.
Multi-Stream Processing Capability. Edge devices successfully process multiple video streams concurrently, supporting complex multi-camera setups at each tracking station. Stream synchronization and resource allocation mechanisms ensure balanced performance across all active streams without degradation.