Schrödinger seeks a Machine Learning (ML) Platform Engineer to join us in our mission to improve human health and quality of life through the development distribution and application of advanced computational methods!

As a member of the Machine Learning team you’ll build scalable software systems that enable scientists and engineers to train deploy and analyze machine learning models at scale. Our machine learning platform LiveDesignML supports applications ranging from molecular property prediction and generative chemistry to protein modeling.

Who will love this job:

• A highly-skilled software engineer who understands coding fundamentals is experienced with Python and has run projects end-to-end from prototype to production
• An ML expert who’s familiar with PyTorch TensorFlow and scikit-learn
• An analytical thinker who enjoys working with multi-dimensional data solving data-processing problems and digging through complex systems to solve technical problems
• A polymath who’s excited about working collaboratively in an interdisciplinary environment and comfortable with self-directed research and problem exploration

What you’ll do:

• Design and develop infrastructure supporting machine learning training inference and experimentation workflows
• Build and maintain production systems that enable scientists to run large-scale ML workloads
• Collaborate with scientists ML researchers and engineers to translate research ideas into reliable software tools
• Contribute to backend services and APIs supporting ML workflows and platform features
• Improve developer workflows testing infrastructure and deployment automation
• Participate in code reviews and contribute to engineering best practices across the team
• Pitch in on frontend components of the ML platform web interface when needed

What you should have:

• BS MS or PhD in Computer Science Machine Learning Software Engineering Mathematics Physics Chemistry or a related field

Experience with the following is nice to have but not required:

• Cloud platforms like AWS or GCP
• Containerization and orchestration (e.g. Docker Kubernetes Argo Workflows  Helm charts etc.)
• CI/CD systems and modern software development workflows (e.g. Jenkins GitHub Actions etc.)
• Monitoring logging or observability systems
• Distributed computing or large-scale ML workloads
• ML training pipelines or experiment management
• Data processing pipelines or large-scale data analysis
• Source control systems (Git or similar)
• Web application development (e.g. React TypeScript REST APIs)
• Interest in scientific computing chemistry biology physics or related domains