Job Description
Roles and Responsibilities:
- Design, develop, and deploy scalable machine learning solutions for real-world applications.
- Collaborate with cross-functional teams to take research ideas to production, ensuring clean, modular, and sustainable code.
- Build and optimize high-performance, data-intensive applications using Python.
- Implement and fine-tune models using modern deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Apply domain expertise in one or more areas such as computer vision, large language models, recommender systems, or operations research.
- Utilize statistical techniques to evaluate model performance and solution feasibility.
- Monitor and maintain deployed ML models and pipelines, ensuring robustness and reliability in production.
Required Qualifications
- Strong proficiency in Python with a focus on high-performance, scalable applications.
- Hands-on experience with at least one modern deep learning framework (e.g., PyTorch, JAX, TensorFlow).
- Deep understanding and practical expertise in one or more ML domains: computer vision, NLP/LLMs, recommender systems, or operations research.
- Solid foundation in statistics and model evaluation methodologies.
- Proven track record of deploying and maintaining ML models in production environments.
- Commitment to writing clean, modular, and maintainable code.
Preferred Qualifications
- Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow).
- Familiarity with cloud platforms (AWS, GCP, Azure).
- Background in research or academic collaboration is a plus.
- Contributions to open-source ML or deep learning projects.
Job Highlights
Responsibilities
- Design, develop, and deploy scalable machine learning solutions for real-world applications
- Collaborate with cross-functional teams to take research ideas to production, ensuring clean, modular, and sustainable code
- Build and optimize high-performance, data-intensive applications using Python
- Implement and fine-tune models using modern deep learning frameworks such as PyTorch, TensorFlow, or JAX
- Apply domain expertise in one or more areas such as computer vision, large language models, recommender systems, or operations research
- Utilize statistical techniques to evaluate model performance and solution feasibility
- Monitor and maintain deployed ML models and pipelines, ensuring robustness and reliability in production