Job Description - Senior Machine Learning Engineer for Computer Vision
We are looking for a talented and experienced Machine Learning Engineer with an entrepreneurial mindset to help us bring advanced AI capabilities into our 3D vision platform. This is a key role at Zivid, where you’ll apply the latest research in computer vision and machine learning to enhance how our 3D cameras perceive and understand the world.
Zivid’s industrial 3D sensors are the best in the business, powering a wide range of robotics and automation applications. We’re small enough to move quickly and act on new opportunities, yet established enough to have deep insight into customer needs and the real problems worth solving. We have strong growth and the line of sight to cut through the hype and deliver machine-learning-enabled vision and robotic manipulation solutions that matter for scaling robotics deployments.
The role
As a Machine Learning Engineer, you will build solutions with next-generation deep learning for vision and robotic manipulation tasks, utilizing Zivid 3D sensors and robotics software. You will apply your skillset in a space with complex, but highly scalable and practical, applications of AI, supported by significant industry investments in compute architectures, simulation, and foundation models. You will be a key member of a core team of ML Engineers at Zivid that owns the R&D efforts to solve vision and automation use cases with AI.
Key responsibilities
Identify opportunities to leverage relevant foundation models or establish starting points for solutions from existing research
Build and curate high-quality datasets for training and evaluation, including data collection, annotation, and curation pipelines
Develop and maintain tools and technologies for building, training, optimizing, and scaling machine learning solutions
Implement and test AI models, and fine-tune models for Zivid’s 3D sensor use cases
Productize ML solutions by identifying gaps and implementing best practices for ML operations
Collaborate with cross-functional teams, including internal and external researchers, vision, optics, and software engineers
Document and share best practices across the organization
Stay engaged with research and communities in the field
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