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 Machine Learning Engineer sits on the Data Science & Machine Learning Team to help the CMG (Commercial, Medical and Government) organization achieve its vision by unlocking value from data quicker and more effectively.
The Machine Learning Engineer will bring a robust understanding of machine learning operations (MLOps) to manage and contribute to our machine learning initiatives.
The 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 ML 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 ML 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.
Who You Are
You hold a Bachelors Degree in Computer Science or related technical disciplineand 3+ years of experience working in a machine learning engineer role, with a minimum of 2 years in an MLOps-focused position.
Your expertise in ML frameworks (e.g., TensorFlow, PyTorch), programming languages (e.g., Python, Scala, Java), MLOps technologies (e.g., Kubeflow, MLflow, AWS Sagemaker), and job scheduling frameworks (e.g., Apache Airflow,AWS Step Functions) is strong.
You have a 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).
Preferred
Experience building and optimizing structured and unstructured big data pipelines, architectures, and datasets.
Excellent communication skills to effectively collaborate with cross-functional teams.
This role offers a hybrid schedule working 3 days per week on campus in South San Francisco, CA.
No relocation benefits are available for this posting.
The expected salary range for this position based on the primary location of CA is $168,100.00 - $312,300.00 USD Annual.Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
