Staff Machine Learning Engineer

Responsibilities

• Architect and Develop ML Systems: Design and implement scalable machine learning pipelines, ensuring robustness and efficiency.
• Model Development: Lead the development, training, and deployment of machine learning models to solve complex business problems.
• Technical Leadership: Guide engineers and data scientists, promoting best practices in ML engineering.
• Productionization: Build and maintain reliable production-grade ML systems, optimizing for performance and scalability.
• Innovation: Stay updated on the latest trends and research in machine learning and artificial intelligence, integrating state-of-the-art techniques when appropriate.
• Code and Review: Write clean, maintainable, well-documented code; conduct code reviews to uphold engineering standards.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. PhD is a plus.
• Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience in evaluating machine learning models in production.
• Experience with data processing frameworks and tools (e.g., Spark, Dask, Pandas, Polars).
• Solid understanding of MLOps principles and tools (e.g. Docker, Kubernetes, model versioning).
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure).
• Proven ability to translate business problems into technical solutions using machine learning.
• Strong communication and collaboration skills, with the ability to articulate complex technical concepts to diverse audiences.