Infrastructure Engineer, Machine Learning
New York
Schrödinger
Schrödinger is the scientific leader in developing state-of-the-art chemical simulation software for use in pharmaceutical, biotechnology, and materials research.We’re seeking a Machine Learning Infrastructure Engineer to join us in our mission to improve human health and quality of life by developing advanced computational methods to transform drug discovery and materials design.
As a member of our Machine Learning team, you’ll work alongside machine learning engineers and scientists committed to deploying data-driven models into production for drug discovery and materials science. Working atop our tech stack (Kubernetes, Argo Workflows, PostgreSQL), you’ll focus on productionizing team prototypes into refined machine learning pipelines for both internal and customer use. This role is essential to delivering high-quality analytic tools based on validated machine learning research to scientists working in medicinal and computational chemistry.
Who will love this job:
- A tooling evangelist who sees manual repetition as wasteful
- An engineer who wants to regularly burst tens of thousands of CPUs and hundreds of GPUs
- A tooling expert who wants to get functionality all the way to internal and external users
- An excellent communicator and documenter
What you’ll do:
- Help speed up research and productionisation of cool projects
- Optimize a containerized development and deployment process for a machine learning web-based platform
- Build scientific validation tools to ensure machine learning models continue to perform as code and data change
- Create tooling to give team members easier access to cloud burst CPUs and GPUs
- Ensure machine learning models can run across multiple platforms on customer machines
What you should have:
- Software engineering experience (in academia or industry) with focus on systems development, integration testing, and library cleanliness
- An understanding of relational databases, REST APIs, stateless applications, and cloud computing.
- Experience using Git for distributed version control and source code management
- Familiarity with continuous integration and continuous deployment practices
- BS, MS, or PhD in Computer Science, Applied Mathematics, ML/Stats, Physics, Chemistry, or a related field
We’d prefer to hire an applicant with some experience in:
- Introducing a containerized system to production on kubernetes
- Extending CI/CD systems for increasing engineering or scientific velocity
- Maintaining and provisioning infrastructure on Google Cloud Platform (GCP)
- Supporting GPUs on cloud systems
- Upholding or building multi-platform tools
- Large-scale distributed computing (pbs, slurm, MPI)
- Classical science - a degree in chemistry, physics, biology, or a related field is a huge plus!
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: APIs Biology Chemistry CI/CD Computer Science Drug discovery Engineering GCP Git Google Cloud Kubernetes Machine Learning Mathematics ML infrastructure ML models PhD Physics Pipelines PostgreSQL RDBMS Research Testing
Perks/benefits: Career development Competitive pay Equity Flex hours Flex vacation Health care Lunch / meals Parental leave Team events
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