Staff Machine Learning Engineer

Data Science at TRACTIAN
The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data. Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies. The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.
What you'll do
We are seeking a highly skilled and experienced Staff Machine Learning Engineer to join our team and lead the development of scalable and innovative ML solutions. As a senior technical leader, you will drive the end-to-end implementation of machine learning systems, mentor engineering teams, and contribute to shaping the strategy for AI/ML initiatives across the organization.

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.