Machine Learning Engineer

We are seeking a highly motivated NLP Engineer to join our team in Tel Aviv.

This role requires expertise in Natural Language Processing (NLP) with an added advantage of experience in Computer Vision. As a NLP Engineer at SignalPET, you will lead the development and implementation of state-of-the-art NLP models and solutions, collaborating with cross-functional teams to drive forward our mission of innovation.

SignalPET is a technology and AI leader in the veterinary field. Using state-of-the-art machine learning techniques and patented technology, SignalPET assists veterinarians in diagnosing and treating companion animals all around the globe.

Key Responsibilities:

• Lead the design, development, and implementation of machine learning models and algorithms with a focus on NLP and LLM or Computer Vision.
• Collaborate closely with cross-functional teams to understand requirements and translate them into scalable machine learning solutions.
• Research, experiment, and innovate in the fields of NLP and LLM or Computer Vision to drive advancements in veterinary diagnostics.
• Develop data pipelines for data collection, preprocessing, and feature engineering to support machine learning initiatives.
• Evaluate and integrate third-party libraries, frameworks, and tools to enhance model performance and efficiency.

Requirements:

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Master's or Ph.D. preferred.
• At least 3 years of experience in NLP, including text classification, sentiment analysis, named entity recognition, and language modeling.
• Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch, spaCy).
• Knowledge of deep learning architectures, such as transformers and recurrent neural networks (RNNs).
• Experience with Computer Vision is a significant advantage.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.
• Ability to work on-site at our Tel Aviv office.