freshteam
Machine Learning Infrastructure Engineer
Zensors
San Francisco, USA
Full-time
Mid
Onsite
Discovered 1 weeks ago
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Free
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Responsibility
- Optimizing Core ML Pipelines: Identifying key bottlenecks in our current video analytics pipeline and performing in-depth analysis to ensure the best possible performance on current server and edge compute architectures.
- Cross-Stack Collaboration: Collaborating closely with AI research and platform engineering teams to optimize core parallel algorithms and influence the design of our next-generation inference infrastructure.
- Model Acceleration: Applying advanced model optimization techniques—such as quantization (Int8/FP16), pruning, and layer fusion—to our Vision Transformers (ViTs) and CNNs to maximize throughput and minimize latency.
- Building Efficient Operators: Working across the entire ML framework/compiler stack (e.g., PyTorch, CUDA, TensorRT, and NVIDIA DeepStream) to write custom optimized ML operator libraries.
- Resource Efficiency: Reducing the compute cost per video stream to enable massive scalability of our SaaS product.
- Data Management: Building, improving, maintaining, and operating systems to facilitate the collection, labeling, and use of visual data for ML training.
Requirements
- BS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.
- Strong programming skills in C/C++ and Python .
- Experience with model optimization , quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).
- Deep understanding of GPU hardware performance , including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.
- Experience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.
- Experience identifying and resolving compute and data flow bottlenecks , particularly in high-bandwidth video processing pipelines.
- Strong communication skills and the ability to work cross-functionally between research and infrastructure teams.
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