Senior Machine Learning Engineer

Key Skills

Java Programming, Python Programming, SQL, Azure, AWS, Scala Programming, PyTorch, TensorFlow, Scikit-learn, SPARK Programming

Job Description

We are seeking Software Engineers who are passionate about marrying data with emerging technologies to join our team. As a JPMorgan Chase Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within the Bank.

What you’ll do in the role:

• Deliver ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art, next generation big data and machine learning applications.
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Use programming languages like Python, Scala, or Java.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Knowledge of executing AI/ML on various document formats is preferable

Basic Qualifications:

• At least 3 years of experience designing and building data-intensive solutions using distributed computing.
• At least 3 years of experience programming with Python, Scala, or Java.
• At least 2 years of on-the-job experience with an industry recognized ML framework (scikit-learn, PyTorch, Dask, Spark, or TensorFlow).
• At least 1 year of experience productionizing, monitoring, and maintaining models.
• Bachelor’s degree or Equivalent

Preferred Qualifications:

• At least 1 year of experience building, scaling, and optimizing ML systems.
• At least 1 year(s) of experience with data gathering and preparation for ML models.
• At least 2 years of experience developing performant, resilient, and maintainable code.
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
• At least 3 years of experience with distributed file systems or multi-node database paradigms.
• Contributed to open source ML software.
• Authored/co-authored a paper on a ML technique, model, or proof of concept.
• At least 3 years of experience building production-ready data pipelines that feed ML models.
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance.