לוח משרות דרושים Computer Vision Engineer בתל אביב יפו

We are seeking a talented and experienced Data and ML Engineer, youll be leading the ML infrastructure and data processing R&D effort, taking a pivotal part in developing our proprietary machine learning-based perception and navigation algorithms.

In this position, you will report to the AI Team Lead and play a key role in a fast-growing team with significant impact and potential for professional and personal growth.

Responsibilities - what you will be doing

Design, build, and manage the data processing pipelines and solutions used for training and testing of AI algorithms
Collaborate closely with perception and navigation R&D team members to implement machine learning algorithms and models tailored for high-accuracy offline execution, later to be used for automatic labeling and semi-supervised training
Profiling, analyzing, and benchmarking of models to identify areas for improvement in terms of efficiency, and accuracy emphasizing aspects of real-time performance on embedded platforms
Stay up to date with the latest advancements in deep learning foundation models and ML algorithms, data management solutions, and cloud computing advancements
Collaborate with the data management and cloud teams to guide and build a unified data management solution based on multi-sensors and our various data sources

As a senior computer vision engineer, you will be part of an elite group in the field of AI microscopy and hematology. Our computer vision team is responsible for solving the most challenging tasks in microscopy image analysis. You and your colleagues will research and develop new AI models to analyze thousands of cells, build new infrastructure for on-premise inference, and collect vast labeled data to provide a clinical-grade solution that runs in real time.

Your responsibilities will include:

Continually bringing new knowledge, experience, ideas, and best practices to the team
Implementing AI based on classic computer vision and deep learning, writing clear and efficient code in Python for smooth productization
Continually improving the teams internal research methods and tools
Supporting team members with their research
Take part in defining team data needs
Collaborating with physics, software, and data teams