ML Engineer

Sanofi - Cambridge, MA

Full-time - Entry Level

Cambridge, MA

10,001+ employees

Chemical Manufacturing

About the position

The Machine Learning Engineer at Sanofi will play a crucial role in leveraging large-scale machine learning systems to enhance the drug discovery process and scale AI solutions for future patients. This position involves working collaboratively within agile teams to design and build cloud-based ML products, supporting the full MLOps lifecycle, and maintaining effective relationships with users to facilitate education and communication regarding ML applications.

Responsibilities

• Work in agile pods to design and build cloud-based ML products with automated CI/CD pipelines that run, monitor, and retrain ML Models.
• Design and implement ML applications in collaboration with computational scientists and data engineers.
• Support the full MLOps lifecycle of new and existing ML applications, including new releases and change management.
• Act as a subject matter expert in ML systems architecture design, developing and maintaining enterprise standards and user guides.
• Build processes for seamless ML integrations, including app monitoring, troubleshooting, lifecycle management, and customer support.
• Maintain effective relationships with the application user base to develop education and communication content.
• Research and gain expertise on emerging tools and technologies, demonstrating enthusiasm for learning.
• Build and evaluate models from internal and external data sources using state-of-the-art ML technologies.
• Collaborate closely with computational scientists, data engineers, software engineers, UX designers, and research scientists focusing on protein therapeutics in an international context.
• Update and report relevant results to interdisciplinary project teams and stakeholders.

Requirements

• MS or PhD in Computer Science, Statistics, Mathematics, Information Systems, Software Engineering, or another quantitative field.
• 1+ years of industry experience preferred, but new graduates will also be considered.
• Experience in data science, statistics, software engineering, modular design, and design thinking.
• Experience developing CI/CD pipelines for AI/ML development and deploying models to production.
• Experience working in an agile pod and supporting cross-functional teams.
• Good understanding of ML and AI concepts with hands-on experience in development.
• Good understanding of deployment and agile lifecycle management of data science applications.
• Ability to work across the full stack and move fluidly between programming languages (e.g., Python, SQL, Spark) and frameworks (e.g., Airflow, MLFlow, Argo).
• Experience with high-performance computing environments.
• Experience with AWS services (e.g., S3, Lambda, SageMaker, CloudWatch, EC2, EFS, FSX).
• Knowledge of relational and non-relational databases.
• Excellent communication skills in English, both verbal and written, with a strong inclination for teamwork.

Nice-to-haves

• Experience managing the lifecycle in a regulated environment.
• Familiarity with protein structure or sequence is a plus.
• Strong understanding of pharma R&D processes is a plus.

Benefits

• Equal opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, or gender identity.