Senior AI/Computer Vision Engineer

United States·San Franciscofull-timesenior

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Wildfire smoke detection Vegetation detection and classification Asset detection (e.g., power lines, utility poles, buildings, roads) Scene understanding and semantic segmentation Spatial reasoning,

Technical Tools

Machine Learning EngineerData

Responsibilities

~1 min read

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Design and implement cloud/edge AI architectures for real-time computer vision applications.
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Develop computer vision models for:
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  Wildfire smoke detection
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  Vegetation detection and classification
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  Asset detection (e.g., power lines, utility poles, buildings, roads)
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  Scene understanding and semantic segmentation
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  Spatial reasoning, including estimating distances and relationships between detected objects and nearby assets
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Develop speech recognition models for fire-related radio communications
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Build lightweight detection, segmentation, classification, and temporal reasoning models for real-time inference.
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Port and optimize deep learning models for ARM64, CUDA, TensorRT, ONNX, and NVIDIA Jetson platforms.
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Build and optimize both cloud and edge inference pipelines for RGB, NIR, PTZ, and multi-camera systems.
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Develop hybrid edge-cloud AI workflows that balance latency, bandwidth, and compute efficiency.
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Improve inference latency, throughput, memory usage, and power efficiency.
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Lead model compression efforts, including quantization, pruning, and knowledge distillation.
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Design deployment, monitoring, OTA update, and observability capabilities for edge AI systems.
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Collaborate closely with AI researchers, software engineers, hardware engineers, data engineers, and product teams.
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Mentor junior engineers and establish best practices for edge AI and computer vision development.

MS or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
5+ years of industry experience in computer vision or machine learning.
Strong experience with PyTorch and modern deep learning architectures.
Experience deploying AI models to edge devices such as NVIDIA Jetson, embedded GPUs, or similar platforms.
Strong understanding of CUDA, TensorRT, ONNX, model optimization, and inference acceleration.
Experience with one or more of the following:
- Object detection
- Semantic or instance segmentation
- Image classification
- Video understanding
- Multi-object tracking
- Depth estimation or 3D computer vision
- Speech recognition
Strong Python and C++ programming skills.

Nice to Have

~1 min read

Experience with outdoor vision systems, autonomous systems, robotics, surveillance, remote sensing, or geospatial AI.
Experience with PTZ camera systems.
Experience with multi-camera calibration, localization, and distributed camera systems.
Experience with spatial AI, scene understanding, or geometric computer vision.
Experience estimating object distances or reasoning about spatial relationships using monocular, stereo, or multi-view imagery.
Experience with MLOps and continuous learning pipelines.
Familiarity with foundation vision models (e.g., DINOv2/DINOv3, SAM, Grounding DINO, Florence, or similar) is a plus.

Final compensation for regular full-time employees is determined by a variety of factors, including job-related qualifications, education, experience, skills, knowledge, and geographic location. In addition to base salary, regular full-time roles are eligible for equity. Benefits are tailored to local market standards and statutory requirements in the employee's country of employment, and may include health coverage, retirement or pension contributions, and paid time off. Specific benefit details will be shared during the interview process.