Senior Machine Learning Engineer

About Us

Cognex is a global leader in the exciting and growing field of machine vision. Our employees, proudly called "Cognoids," are passionate about solving the most difficult vision problems with our embedded cameras and software products featuring state-of-the-art , 2D and 3D vision technology. Our Work Hard, Play Hard, Move Fast culture recognizes achievement and dedication with unique rewards and celebrations.

We are looking for creative, bright, motivated Cognoids who share our passion for excellence and want to make an impact at a dynamic, global company. We celebrate our employees for their innovation, perseverance, and hard work in a fun, rewarding, and quirky environment. If you enjoy the sense of accomplishment that comes from working together to create products that solve tough problems for organizations around the world, contact us to see how you can become part of our team!

This is a hybrid role based in our Natick, MA headquarters.

You will join the Core Vision Tools team that is responsible for building state-of-the-art computer vision algorithms and deep learning models used in all Cognex's products. The team works across custom hardware, optimized embedded systems, and next-generation algorithm design to deliver high-performance solutions that run at extreme speeds and scale globally across product lines.

We are seeking an experienced AI/ML engineer with strong research and development skills—someone deeply familiar with designing, training, evaluating, and deploying efficient deep learning models for computer vision. You will advance Cognex's AI capabilities by developing novel architectures, optimizing models for embedded platforms, and collaborating with R&D and product teams to bring research prototypes into production.

• Research, design, and implement efficient deep learning models for industrial machine vision tasks, with a focus on algorithms with low power, low latency and data efficiency requirements.
• Collaborate with cross-functional engineers to transition experimental AI models into production-ready components for embedded systems.
• Develop high-performance Python and C/C++ code for training, optimization, benchmarking, and deployment.
• Lead model architecture discussions and make long-term technical decisions across platforms.
• Optimize neural networks for resource-constrained environments (quantization, pruning, distillation, hardware-aware design).
• Build evaluation pipelines, datasets, and tools to assess model accuracy, robustness, runtime performance, and reliability.
• Diagnose and resolve complex issues across hardware, software, and ML components.
• Provide technical guidance to engineers developing UIs, test frameworks, and runtime components.
• Mentor junior engineers and champion engineering excellence in ML research and development.

• Industry or academic experience developing and optimizing deep learning algorithms in one or more relevant technical areas—computer vision, natural language processing, speech recognition.
• Deep understanding of AI concepts including training strategies, loss functions, evaluation metrics, and ML operations.
• Strong Python programming skills.
• Proficient C/C++ experience for performance-critical systems.
• Proficiency with ML frameworks (PyTorch, Tensor Flow), model optimization, and ML development life cycles.
• Strong debugging and analytical problem-solving skills.
• Experience with software development practices including version control, CI/CD, and issue tracking.
• Excellent communication and collaboration skills.

• Background in computer vision, signal processing, or related fields.
• Experience with embedded ML, quantization, or hardware-aware optimization.
• Hands-on experience building and deploying efficient deep learning models for real-world computer vision applications.

Bachelor's or Master's degree in Computer Science, Computer/Electrical Engineering, Mathematics or related field, and 5+ years of relevant experience in AI/ML, software engineering, or applied research roles.

Cognex believes in fair and equitable pay. A reasonable estimate of…