Machine Learning Engineer Generative AI (hold)
Summary
ML Engineer at a food-tech company in Haifa, leading R&D of generative AI models for plant-based recipe generation. The role covers the full ML pipeline — data acquisition, preprocessing, training, and validation — using deep learning frameworks like TensorFlow, PyTorch, and Keras, with geometric ML and graph neural networks.
Job Summary:
Join our cutting-edge food-tech company that pioneers plant-based food creations using
data-driven methods. We're looking for an experienced and passionate Machine Learning
Engineer to drive our R&D initiatives. This role primarily focuses on the research, prototyping,
and development of state-of-the-art data-driven algorithms and theoretical solutions, especially
in the domains of geometric machine learning and generative methods for recipe generation.
Key Responsibilities:
- Research & Development: Lead R&D initiatives by exploring, prototyping, and developing
state-of-the-art generative AI models for recipe generation.
- End-to-End ML Pipeline: From data acquisition and preprocessing to model training and
validation, be the mastermind behind our innovative prototypes.
- Data Analysis & Visualization: Dive into complex datasets to uncover opportunities and
insights, shaping the direction of our research efforts.
- Data Processing: Design pipelines for handling large-scale and unstructured data, ensuring it's
ready for high-quality modeling.
- Collaboration: Work hand-in-hand with data scientists to refine and enhance machine learning
models, pushing the boundaries of what's possible.
- Robust Prototyping: Ensure that all prototypes are scalable, efficient, and ready for further
refinement or production consideration.
Minimum Qualifications:
- A bachelor degree in math, engineering, computer science, or a related field.
- Mathematical maturity.
- 4+ years of hands-on experience in Machine Learning R&D or a similar research-focused role.
- Solid foundation in data structures, data modeling, and software architecture.
- Profound understanding of deep learning theories, architectures, and libraries such as
TensorFlow, PyTorch, and Keras.
- Experience or interest in graph neural networks or similar techniques is a major plus.
