Principal Machine Learning Engineer

Genentech•Daly City, CA

1d•Onsite

About The Position

The Principal Machine Learning Engineer leads the strategic design and development of advanced machine learning models, driving innovation and exploring emerging technologies. This role involves overseeing the entire lifecycle of ML models, ensuring they meet business and regulatory standards, and collaborating with cross-functional teams to integrate these models into existing systems. The Principal Machine Learning Engineer writes scalable, production-ready code, ensures models are explainable and robust, and contributes to the company's machine learning architecture.

Requirements

8 years of experience working in a machine learning engineer role or related experience.
Bachelor's or Master's Degree in Computer Science or related discipline is preferred.
Expert in ML frameworks and a proven track record of leading complex ML projects.
Expertise in ML frameworks like TensorFlow, PyTorch, Scikit-learn, etc.
Solid understanding of statistical methods and machine learning algorithms.
Proficient with software engineering best practices, including agile development, code reviews, software change management, build processes, and testing.
Ability to navigate in a cross-functional environment with appropriate agile-based approaches for sprint planning, backlog grooming, and timelines tracking.
Ability to translate complex concepts into simple, easy-to-understand content for a non-technical audience.

Nice To Haves

Extensive experience in designing and implementing cutting-edge data architectures and pipelines.
Recognized expertise in the application of ML in highly regulated industries, with a focus on strategic impact.
Experience building and optimizing structured and unstructured big data pipelines, architectures, and datasets.
Excellent communication skills to effectively collaborate with cross-functional teams.
Experience in healthcare, pharmaceutical, or highly regulated industries.

Responsibilities

Independently leads the strategic design and development of machine learning (ML) models across multiple projects.
Innovate with different ML algorithms and architectures to optimize performance.
Push the boundaries of machine learning, exploring emerging technologies for potential integration.
Oversee the entire lifecycle of Machine Learning (ML) models, from conception to deployment, ensuring they meet business and regulatory standards.
Use feature engineering to prepare input data for building ML models and improving the accuracy and performance of those models.
Write efficient, scalable, and production-ready code for ML models, to be scaled and productionalized in partnership with ML Ops Engineer.
Collaborate with data scientists to transition models from research to production with support from data leads, ML Operations, and Informatics (IX) team.
Ensure ML models are explainable, fair, and robust.
Use ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
Collaborate with data scientists and data science product owners/managers to translate business requirements into ML models.
Manage risks and dependencies and proactively address any challenges that arise.
Contribute to the company's machine learning architecture in partnership with the IX team to support scalable and repeatable model training and deployment.
Comply with all laws, regulations and policies that govern the conduct of Genentech activities.