Senior Machine Learning Engineer

Roche • South San Francisco, CA

1d

About The Position

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. The Opportunity 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. The Senior Machine Learning Engineer sits on the Data Science Team within our Strategic Analytics & Intelligence Organization (SAI) and exists to help the CMG (Commercial, Medical and Government) organization achieve its vision by unlocking value from data quicker and more effectively. As a center of excellence, the Commercial Data Science team leverages advanced data science capabilities, working across CMG to develop strategic and holistic solutions by identifying and leading innovative analytic projects & pilots to enable patient and customer solutions. The Senior Machine Learning Engineer will bring a robust understanding of machine learning operations (MLOps) to manage and contribute to our machine learning initiatives. The Senior Machine Learning Engineer will design, enhance, scale, and maintain machine learning solutions from conception to deployment in a production environment. You will collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable ML systems. You will also architect and uphold MLOps pipelines using job scheduling frameworks to streamline data preparation, training, deployment, and machine learning model lifecycles. In addition to ensuring best practices in code quality, version control, and CI/CD for machine learning pipelines, you will also design and implement robust monitoring systems for deployed models to track performance, data drift, and anomalies. The MLOps Engineer will also pioneer and administer model retraining, A/B testing, and progressive deployment strategies for continuous model enhancement. You will contribute to the company's machine learning architecture to support scalable and repeatable model training and deployment. Facilitate the creation of automated processes for model validation and testing.

Requirements

• Bachelor’s Degree in Computer Science or related technical discipline
• 7+ years of experience working in a machine learning engineer role
• Minimum of 2 years in an MLOps-focused position
• Expertise in ML frameworks (e.g., TensorFlow, PyTorch)
• Expertise in programming languages (e.g., Python, Scala, Java)
• Expertise in MLOps technologies (e.g., Kubeflow, MLflow, AWS Sagemaker)
• Expertise in job scheduling frameworks (e.g., Apache Airflow,AWS Step Functions)
• Solid understanding and experience with cloud services and containerization technologies/platforms, particularly AWS and Kubernetes.
• Proficient with software engineering best practices, including agile development, code reviews, SCM, build processes, testing, and operations.
• Experience with distributed computing and big data technologies (e.g., Hadoop, Spark).

Nice To Haves

• Masters Degree in Computer Science or related discipline
• Experience building and optimizing structured and unstructured big data pipelines, architectures, and datasets.
• Excellent communication skills to effectively collaborate with cross-functional teams.

Responsibilities

• Design, enhance, scale, and maintain machine learning solutions from conception to deployment in a production environment.
• Collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable ML systems.
• Architect and uphold MLOps pipelines using job scheduling frameworks to streamline data preparation, training, deployment, and machine learning model lifecycles.
• Ensure best practices in code quality, version control, and CI/CD for machine learning pipelines
• Design and implement robust monitoring systems for deployed models to track performance, data drift, and anomalies.
• Pioneer and administer model retraining, A/B testing, and progressive deployment strategies for continuous model enhancement.
• Contribute to the company's machine learning architecture to support scalable and repeatable model training and deployment.
• Facilitate the creation of automated processes for model validation and testing.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level