About the role

Schrödinger is on the cutting edge of computer-aided drug discovery and materials science. Our software platform continues to revolutionize the design of therapeutics and materials, and we leverage this platform to advance a pipeline of internal, wholly-owned drug discovery programs. We also license our software to leading biopharmaceutical and industrial companies, academic institutions, and government laboratories worldwide.

We're seeking a motivated Software Infrastructure Engineer to join our team in Hyderabad. You'll play a key role in automating the deployment and maintenance of our Kubernetes-based application (LiveDesign) on Google Cloud Platform (GCP).

This is an excellent opportunity for someone who enjoys automation using modern approaches such as GitOps and Infrastructure as Code, and is excited to learn and grow in a collaborative environment. You'll work directly with other teams, regularly demo new features you build, and help shape deployment workflows that make their work more efficient.

What You'll Work On

Primary Focus:

Build and improve GitHub Actions workflows to automate deployment pipelines
Maintain and enhance Terraform infrastructure code for GCP resources
Develop Python scripts and tools for infrastructure automation
Collaborate with other internal teams to understand their needs

Learning & Growth Areas:

Improve Kubernetes Helm charts and establish best practices
Design, implement and/or improve microservices, in particular related to job orchestration of scientific computations
Improve and extend the existing infrastructure used for user authentication
Optimize Docker configurations for our applications
Potentially contribute to our deployment manager web application (Node.js/React) if interested

Required Skills

1-3 years of professional software development or DevOps experience
Programming: Strong Python skills (writing scripts, working with APIs, testing)
Cloud Platform: Experience with Google Cloud Platform. Experience with other cloud providers (AWS, Azure) is a plus.
Automation: Familiarity with CI/CD concepts and tools
Version Control: Comfortable with Git and GitHub workflows
Communication: Strong English communication skills; ability to demo work and collaborate with team members

Nice-to-Have Skills

We'd especially value candidates with:

Experience in writing GitHub Actions workflows or similar CI/CD pipelines
Infrastructure as Code experience, preferably Terraform / OpenTofu
Kubernetes fundamentals (deploying applications, understanding pods/services)
Helm chart experience
Bash/shell scripting proficiency
Docker containerization basics
Web application development skills (esp. Node.js and React)

Important: Don't worry if you don't have all these skills! We value curiosity and willingness to learn. If you're strong in Python and cloud platforms and excited to develop expertise in GitHub Actions, Terraform, and Kubernetes, we encourage you to apply.

What Makes This Role Unique

Learning opportunities: We'll support your growth in areas like Kubernetes, Helm and Terraform
Impact: Your automation work will directly reduce manual deployment overhead. You'll work closely with other teams (Solutions Architecture team, Quality Analysis team), building tools that directly improve their daily work
Collaboration: Regular interaction with other teams to understand needs and demo features.
Modern stack: Work with current technologies and cloud-native patterns

About Our Team

We value:

Collaboration and communication - sharing knowledge and learning together
User focus - understanding the needs of the teams we support
Quality - writing maintainable, tested code with proper documentation
Growth mindset - eagerness to learn new technologies and approaches

As an equal opportunity employer, Schrödinger hires outstanding individuals into every position in the company. People who work with us have a high degree of engagement, a commitment to working effectively in teams, and a passion for the company's mission. We place the highest value on creating a safe environment where our employees can grow and contribute, and refuse to discriminate on the basis of race, color, religious belief, sex, age, disability, national origin, alienage or citizenship status, marital status, partnership status, caregiver status, sexual and reproductive health decisions, gender identity or expression, or sexual orientation. To us, "diversity" isn't just a buzzword, but an important element of our core principles and key business practices. We believe that diverse companies innovate better and think more creatively than homogenous ones because they take into account a wide range of viewpoints. For us, greater diversity doesn't mean better headlines or public images - it means increased adaptability and profitability.

Skills & Tags

react node python go aws gcp azure kubernetes

Aplyr's read

Schrödinger leverages computational modeling to innovate in drug discovery and materials science, attracting scientists and tech experts passionate about cutting-edge research.
Synthesized from recent postings & public sources

What's promising

•Schrödinger is at the forefront of computational drug discovery, offering significant innovation potential.
•The company provides a collaborative environment for scientists and technologists to solve complex problems.
•Schrödinger's global presence allows for diverse career opportunities across various scientific and technical roles.

What to watch

•High reliance on computational methods may limit applicability in some real-world scenarios.
•The niche focus on modeling requires specialized skills, potentially narrowing job applicant pools.
•Rapid technological changes in biotech could outpace Schrödinger's current capabilities.

Why Schrödinger

•Schrödinger integrates advanced computational techniques with traditional scientific methods.
•The company's dual focus on drug discovery and materials science is relatively rare in the industry.
•Schrödinger's software solutions are pivotal in reducing time and cost in the R&D process.

Aplyr’s read is generated by AI from public sources. Was it useful?