Computer Vision Engineer

About the role

This position is centered on developing and deploying advanced computer vision systems for wildfire detection and environmental monitoring within a fast-growing AI and IoT organization. The hire will own the implementation and optimization of models that process ultra-high-definition imagery from 360-degree cameras and other sensors in real time. You will work directly with AI researchers to translate novel algorithms into reliable production software on cloud and edge infrastructure. A key responsibility is to ensure computer vision models accurately detect smoke, classify vegetation, and recognize assets under varied environmental conditions. The role emphasizes curiosity and adaptability as you learn across the full AI stack from data pipelines to embedded inference. You will also own tools for data visualization, benchmarking, and monitoring to support rapid iteration and model quality. This is an excellent opportunity for an engineer who enjoys deep technical challenges and wants to grow into a senior technical contributor role on a impactful mission.

Key facts

What you'll do

• Assist in developing computer vision models for wildfire smoke detection, vegetation detection and classification, asset detection and recognition, instance and semantic segmentation, scene understanding, and spatial reasoning.
• Help implement and maintain scalable machine learning and computer vision pipelines that process high-resolution imagery and sensor data reliably.
• Assist with deploying and optimizing AI models on NVIDIA Jetson and other edge platforms to meet latency, memory, and power constraints in field environments.
• Support model optimization efforts, including TensorRT conversion, quantization, and inference acceleration to improve throughput and efficiency.
• Build and maintain tools for data processing, visualization, benchmarking, evaluation, and monitoring to enable data-driven decisions and model improvements.
• Conduct experiments to measure model performance, analyze failure modes, and present findings clearly to cross-functional teams for rapid iteration.
• Debug inference, deployment, networking, and hardware integration issues across cloud and edge environments to ensure system stability and uptime.
• Contribute to continuous learning, model evaluation, and data quality improvement workflows that strengthen long-term system performance.
• Collaborate closely with AI researchers, software engineers, and product teams to align technical solutions with evolving product requirements.
• Take ownership of end-to-end development tasks, from prototyping ideas to productionizing robust computer vision features in customer-facing systems.
• Participate in code reviews, documentation, and knowledge sharing to maintain high engineering standards across the team.
• Help define and track key metrics for model accuracy, latency, and operational health to guide prioritization and investment.

Requirements

• Hold a Bachelor's, Master's, or PhD degree in Computer Science, Electrical Engineering, Computer Engineering, or a closely related technical field.
• Demonstrate strong foundational knowledge in computer vision, machine learning, and deep learning concepts and practical methods.
• Bring hands-on experience with modern computer vision frameworks and libraries such as PyTorch, TensorFlow, OpenCV, and related tooling.
• Show proficiency in one or more programming languages commonly used for AI and systems development, with a strong ability to write clean, maintainable code.
• Possess experience with model optimization and deployment techniques, including quantization, TensorRT, and edge inference on constrained hardware.
• Have a solid understanding of neural network architectures, loss functions, and evaluation metrics relevant to detection, segmentation, and recognition tasks.
• Exhibit strong debugging and analytical skills to investigate issues in data, models, inference pipelines, and hardware integration.
• Communicate effectively in written and verbal form to collaborate with both technical and non-technical stakeholders in a fast-paced environment.

Nice to have

• Experience with NVIDIA Jetson platforms and related edge AI software stacks.
• Familiarity with large-scale image datasets, annotation tools, and data pipeline automation.
• Knowledge of cloud infrastructure, containerization, and orchestration tools used for AI deployment.
• Contributions to open source computer vision or AI projects that demonstrate engineering rigor.
• Background in remote sensing, environmental monitoring, or wildfire-related applications.

Practical notes

The role is full-time and based in San Francisco, California. The position is hybrid-remote with an expectation to work from the San Francisco office regularly while supporting occasional remote work within policy guidelines.