ML Engineer

Sanofi - Cambridge, MA

Full-time - Entry Level

Cambridge, MA

10,001+ employees

Chemical Manufacturing

About the position

As a Machine Learning Engineer at Sanofi, you will play a pivotal role in our ambitious digital transformation program, particularly within the Biologics x AI Transformation initiative. This initiative aims to leverage artificial intelligence (AI) and machine learning (ML) to enhance our research and development processes, manufacturing, and commercial performance. Your primary focus will be on designing and building cloud-based ML products that integrate seamlessly into our existing systems, thereby accelerating the drug discovery process and improving patient outcomes. You will work in agile pods, collaborating with cross-functional teams that include computational scientists, data engineers, and software engineers. Your responsibilities will encompass the full MLOps lifecycle, from developing and maintaining CI/CD pipelines to deploying and monitoring ML models in production. You will also serve as a subject matter expert in ML systems architecture, ensuring that enterprise standards are upheld and that user guides and FAQs are readily available for application users. In this role, you will be expected to build and evaluate models using both internal and external data sources, employing state-of-the-art ML technologies. Your ability to communicate effectively with interdisciplinary project teams and stakeholders will be crucial as you update and report on relevant results. Additionally, you will be encouraged to research and gain expertise in emerging tools and technologies, fostering a culture of continuous learning and improvement within the team.

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, developing and maintaining enterprise standards, user guides, release notes, and FAQs.
• Build processes that support 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 and innovation.
• Build and evaluate models from internal and external data sources, algorithms, and simulations 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.
• Ideally 1+ years of industry experience; 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 supporting and collaborating with 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 the pharmaceutical R&D process 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.