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Computer Vision CV/ML Engineer - Seattle Washington
Company: Creative Solutions Services, LLC
Location: Seattle, Washington
Posted On: 02/02/2025
Job Title: Computer Vision / Machine Learning (CV/ML) EngineerLocation: Remote/ Pacific Time ZoneClearance Required: Public Trust EligibleSalary: $110K-$125KApplication Deadline: March 31, 2025To apply, please follow these steps: Visit . Select the position you are interested in. Review the job details, then click Apply Now. Complete and submit your application.DescriptionIBSS is looking for a full-time or part-time Computer Vision / Machine Learning Engineer to assist the AFSC Midwater Assessment and Conservation Engineering team in creating solutions that aid AFSC efforts to reduce bycatch, ensure sustainable fishing, and protect marine life. We are seeking a Computer Vision / Machine Learning (CV/ML) engineer to develop algorithms and software solutions to assist with achieving this goal.You will build models to automate species identification and length measurements from images of fish caught. The goal is to use advanced AI / Artificial Intelligence and Client techniques to revolutionize fisheries conservation, with projects involving tasks such as fish detection and tracking using cloud and edge computing, as well as fish age estimation through deep learning using otolith images.Common tasks will include object detection and semantic segmentation including using current paradigms such as few/zero-shot learning. Knowledge in other aspects of ML, such as using off-the-shelf OCR and voice-to-text models, is additionally valuable.Key Responsibilities: - Developing Client and AI Methods: Collaborate with the IBSS & AFSC team on developing and monitoring automated detection systems.
- Deep Learning Expertise: Train and implement deep learning models to detect and count fish in video frames. Utilize appropriate object detection and tracking methods in PyTorch and TensorFlow.
- Implementing Innovative AI/Client Solutions: Apply cutting-edge technologies to enhance fisheries management. Utilize self-supervised learning and adapt models to various computing resources.
- Transfer Learning & Sustainability: Transfer learning is used to accelerate development and PyTorch to facilitate efficient solutions.
- Evaluate and Optimize Models: Oversee evaluation and optimization in TensorFlow or PyTorch for seamless deployment on edge computing devices.
- Integrate current techniques to enhance workflow depending on the needs of the project, such as few-shot learning where limited annotations are available.
- Container Deployments: Converting intricate, deep learning models into container deployments integrating with Tator.io, a comprehensive video and image annotation and review software platform.
- Maintaining Technical Knowledge and Expertise: Stay current on the latest developments in deep learning, edge computing, container applications, and cloud computing to enhance the effectiveness of the project.
- Collaborating with Cross-Functional Teams: Work with cross-functional teams to ensure successful model deployment while proactively addressing any challenges that may arise.
- Collaborating with Scientific Groups: Work effectively with scientific groups with ML needs that have substantial domain knowledge (e.g., marine biology) but less knowledge of the advantages and limitations of ML.
- Ensure Best Practices: Follow best practices in all stages of development.
- Documentation: Thoroughly document each stage of the development process, from the initial model design to deployment and ongoing maintenance. This is essential for tracking the project's progress, troubleshooting, and future improvements.Required Skills /Education/ Certifications & Qualifications:
- Master's or PhD in Computer Science or similar with a focus on Computer Vision or Machine Learning. Relevant bachelor's degree with additional years of experience will be considered.
- 3+ years of experience in building and deploying deep learning models in Python.
- Proficient in machine learning optimization, software development for real-time systems, container applications, and cloud computing.
- Proficiency with common CV libraries like OpenCV, TensorFlow, Keras, PyTorch.
- Experience deploying models on edge devices with size, weight, and power constraints.
- Expertise with Python, OpenCV, Linux, Windows, Docker, Git, and PyTorch.Desired Skills:
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