Infrastructure Engineer, Machine Learning

Key Skills

Continuous Integration & Continuous Delivery - CI/CD, Database, Google Cloud Platform (GCP), GPUs, Infrastructure as code, Machine learning techniques, Optimization, REST API

Job Description

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!

Pay and perks:

Schrödinger understands it’s people that make a company great. Because of this, we’re prepared to offer a competitive salary, equity-based compensation, and a wide range of benefits that include healthcare (with dental and vision), a 401k, pre-tax commuter benefits, a flexible work schedule, and a parental leave program. We have regular catered meals in the office, a company culture that is relaxed but engaged, and over a month of paid vacation time. Our Office Management team also plans a myriad of fun company-wide events. New York is home to our largest office, but we have teams all over the world. Schrödinger is honored to have been included in Crain's New York Best Places to Work, BuiltIn's NYC Best Place to Work, and Newsweek's list of America's 100 Most Loved Workplaces.