Sanofi - Cambridge, MA

posted about 1 month ago

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.
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