Principal Machine Learning Engineer

Why Genentech

​​​​​​​We’re passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world’s most complex health challenges and transforming society.

Who We Are

Our Data, Analytics, and AI team is dedicated to solving complex healthcare challenges and improving patient outcomes. Data, Analytics, and AI empowers business partners across Commercial, Medical, and Government Affairs (CMG) to make impactful decisions by leveraging data, analytics, business products, and AI/ML to enable fast, targeted actions in rapidly evolving business contexts.

Data, Analytics, and AI fosters a unified understanding of customers, actions, and outcomes by integrating analytics and insights seamlessly into CMG’s evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos.

In Data, Analytics, and AI, you will work as a trusted, objective advisor and expert, recommending critical decisions and actions to be taken with credibility and a focus on driving measurable impact. You will be part of a thriving culture built on collaboration and innovation.

Job Summary

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. 

Key Job 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.

Who You Are

Minimum Candidate Qualifications & Experience

• 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 conten