ashby
Software Engineer - ML Infrastructure
Specter
San Francisco, USA
Full-time
Mid
Onsite
Discovered 1 weeks ago
PyTorchTensorFlowTensorRTONNX RuntimeOpenVINOWeights & Biases
Free
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PyTorchTensorFlowTensorRT
Key Skills for This Role
PyTorchTensorFlowTensorRTONNX RuntimeOpenVINOWeights & Biases
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Role + Responsibilities
- Specter is hiring an ML infrastructure engineer to build and scale the machine learning systems that power real-time perception and inference across our edge-cloud platform. This role owns the training, deployment, and optimization of computer vision and sensor fusion models that enable autonomous monitoring and decision-making for our customers' physical assets.
Key responsibilities include
- Designing and implementing scalable ML training pipelines for computer vision models (object detection, tracking, classification, segmentation).
- Building efficient model serving infrastructure for real-time inference on edge devices with constrained compute and power budgets.
- Optimizing models for deployment on embedded hardware (quantization, pruning, TensorRT, ONNX, CoreML).
- Developing continuous training and evaluation systems to improve model performance from production data feedback loops.
- Creating data pipelines for ingesting, labeling, versioning, and managing massive multi-modal sensor datasets (video, radar, lidar, thermal).
- Implementing model monitoring, A/B testing frameworks, and performance analytics for deployed perception systems.
- Collaborating with perception researchers to transition models from research to production at scale across thousands of edge nodes.
- Building tools and infrastructure for distributed training, hyperparameter optimization, and experiment tracking.
Preferred Qualifications
- Strong experience with ML frameworks (PyTorch, TensorFlow) and model optimization tools (TensorRT, ONNX Runtime, OpenVINO).
- Deep understanding of computer vision architectures and their deployment tradeoffs (YOLO, transformers, CNNs, real-time detection/tracking).
- Hands-on experience deploying models on edge devices (NVIDIA Jetson, ARM processors, or similar embedded platforms).
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