Responsibilities:

• Design, develop, and deploy machine learning models for factory and warehouse environments.
• Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.
• Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.
• Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.
• Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.
• Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.
• Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

 Minimum Requirements:

• In-depth knowledge of Python for high-performance, data-intensive applications.
• Proficiency with at least one modern deep learning framework (e.g., PyTorch, Jax, TensorFlow).
• Expertise in one or more of the following areas: computer vision, large language models, recommender systems, or operations research.
• Foundational knowledge of statistics for model comparison and performance assessment.
• Real-world experience deploying and maintaining machine learning solutions in production environments.
• Passion for clean, sustainable, and modular code to bring research concepts to practical implementation.

 Preferred Qualifications:

• Experience working in manufacturing, industrial automation, or warehouse environments.
• Familiarity with multi-modal data integration and analysis.
• Strong problem-solving skills and the ability to thrive in ambiguous, fast-paced settings.
• Excellent communication skills for cross-functional teamwork.