AI/Computer Vision Engineer

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  Assist in developing 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 and recognition

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    Instance and semantic segmentation

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    Scene understanding and spatial reasoning

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  Assist in developing speech recognition models for fire-related radio communications

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  Help implement and maintain machine learning and computer vision pipelines.

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  Assist with deploying and optimizing AI models on NVIDIA Jetson and other edge platforms.

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  Support model optimization efforts, including TensorRT conversion, quantization, and inference acceleration.

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  Build tools for data processing, visualization, benchmarking, evaluation, and monitoring.

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  Conduct experiments, analyze model performance, and present findings to the team.

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  Debug inference, deployment, networking, and hardware integration issues.

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  Contribute to continuous learning, model evaluation, and data quality improvement workflows.

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  Collaborate closely with AI researchers, software engineers, hardware engineers, and product teams.

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  Document experiments, engineering decisions, and best practices.

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  Take on a variety of technical challenges as needed and continuously expand your skills across computer vision and cloud/edge AI.

• BS or MS in Computer Science, Electrical Engineering, Robotics, or a related field.

• 1–3 years of experience (including internships or research) in software engineering, machine learning, or computer vision.

• Experience with Python and deep learning frameworks such as PyTorch.

• Understanding of machine learning fundamentals and modern computer vision techniques.

• Familiarity with Linux development environments.

• Strong problem-solving skills, curiosity, and a desire to learn.

• Excellent communication and teamwork skills.

• Experience with NVIDIA Jetson, CUDA, TensorRT, ONNX, or embedded AI platforms.

• Experience with OpenCV.

• Experience with one or more of the following:

  • Object detection

  • Instance or semantic segmentation

  • Image classification

  • Multi-object tracking

  • Video understanding

  • Speech recognition

• Familiarity with vision foundation models such as SAM, Grounding DINO, or DINO is a plus.

• Experience with cloud platforms, MLOps, or CI/CD workflows.

• Interest in deploying AI systems in real-world environments, particularly outdoor vision systems.

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.