Senior Machine Learning & Computer Vision Engineer

Danaher•Bellevue, WA

4d•Onsite

About The Position

The Senior Machine Learning & Computer Vision Engineer is responsible for the development of industrial-grade applications by leveraging advanced machine learning and computer vision techniques to analyze complex biological images. You will play a key role in architecting, developing, and optimizing software solutions that meet the demanding performance requirements of the life science industry. This position reports to the software engineering manager and is part of the IN Carta software development team located in Bellevue, WA and will be an on-site role.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s or Ph.D. preferred.
7+ years of professional software development experience, with a strong focus on industrial application development.
Proven expertise in developing machine learning and computer vision solutions, preferably in the life sciences or a related field.
Demonstrated experience in performance optimization techniques, including the use of GPU acceleration and parallel processing.
Proficiency in programming languages such as Python and C/C++.
Extensive experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, OpenCV).
Strong understanding of computer vision algorithms and image analysis techniques.
Expertise in optimizing code for performance and scalability in high-demand industrial applications.
Familiarity with software development best practices, version control systems (e.g., Git), and agile methodologies.
Understanding of life science imaging techniques (e.g., microscopy, medical imaging, high-content screening) and the associated data challenges.
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration abilities to work effectively with cross-disciplinary teams.
Ability to manage multiple projects concurrently and deliver high-quality results under tight deadlines.

Nice To Haves

Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) is a plus.
Knowledge of regulatory standards and data privacy requirements in the life sciences is an advantage.
Experience in deploying machine learning models in production environments.
Publications or contributions to open-source projects in computer vision or machine learning.
Familiarity with DevOps practices and CI/CD pipelines.

Responsibilities

Design, develop, and maintain high-performance machine learning and computer vision applications tailored for life science imaging challenges.
Architect robust, scalable, and maintainable software systems that integrate seamlessly with existing industrial workflows.
Collaborate with cross-functional teams, including data scientists, bioinformaticians, and domain experts, to translate research prototypes into production-ready systems.
Develop and implement state-of-the-art algorithms (e.g., convolutional neural networks, object detection, segmentation) to process and analyze biological image data.
Optimize existing machine learning models and computer vision pipelines for performance, accuracy, and scalability.
Stay current with the latest research and advancements in computer vision, deep learning, and AI as they relate to life sciences.
Identify performance bottlenecks in software and implement optimization strategies to enhance processing speed and efficiency.
Leverage parallel computing, GPU acceleration, and efficient data management techniques to ensure high throughput and low latency.
Conduct rigorous testing and benchmarking to validate performance improvements and ensure software reliability in industrial environments.
Ensure the software meets industrial standards for reliability, security, and compliance with regulatory requirements specific to the life science domain.
Develop comprehensive documentation, including technical specifications, user guides, and maintenance procedures.
Participate in code reviews, pair programming, and agile development practices to maintain high code quality and efficient development cycles.
Work closely with research teams and other stakeholders to understand user requirements and translate them into technical solutions.
Contribute to a culture of continuous improvement by sharing best practices, tools, and techniques across the team.