Machine Learning Platform Engineer
Quick Summary
Design and develop infrastructure supporting machine learning training, inference,
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.
- 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
Responsibilities
~1 min read- →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
- BS, MS, or PhD in Computer Science, Machine Learning, Software Engineering, Mathematics, Physics, Chemistry, or a related field
Nice to Have
~3 min read- 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
Listing Details
- Posted
- March 12, 2026
- First seen
- March 26, 2026
- Last seen
- April 20, 2026
Posting Health
- Days active
- 25
- Repost count
- 0
- Trust Level
- 34%
- Scored at
- April 21, 2026
Signal breakdown
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