Job Description
Full-Stack Solution Design: Own the design and implementation of scalable, cloud-native applications that meet scientists’ requirements end to end.
AI/ML Data Products: Deliver production-grade data products that enable and accelerate AI/ML use cases in discovery.
Product and UX Innovation: Build novel features that solve long-standing drug discovery problems with experiences that feel seamless and unified.
Cross-Functional Collaboration: Work closely with product, design, data science, and scientific teams to build cutting-edge services and user journeys.
Architecture and Data Models: Propose and implement changes to data models, core architecture, and the codebase to improve quality and velocity.
Full-Stack Delivery: Contribute across all layers of the stack, even where you are less experienced, to move the product forward.
Agile Ways of Working: Advance modern, agile software practices and help foster a vibrant engineering culture.
Platform Reliability: Plan, implement and support core infrastructure to improve scalability, reliability, performance, and availability.
Engineering Excellence: Champion rigorous practices including code reviews, automated testing, logging, monitoring, and alerting.
Learning and Mentorship: Stay on top of tech trends, experiment, engage with internal and external communities, and mentor peers.
Critical Problem Solving: Apply structured analysis and sound judgment to propose robust solutions to engineering challenges.
Key Responsibilities
Experience in designing end-to-end full stack software in cloud
Deep expertise in Java and Python. Additionally, expertise in other programming languages like C++, Node.js will be advantageous
Experience in at least one major web development framework from Spring, Flask, Django, and beyond
Strong front end skills with one of the major front end frameworks from React, Angular or Vue.js
Additional front-end skills in CSS as well as some related CSS framework like Bootstrap
Experience working with relational and/or NoSQL databases and knowledge of query optimization techniques
Proficiency in Linux environments
Demonstrable high proficiency in data structures and design patterns, as well as associated antipatterns. Be able to defend, compare, and contrast these decisions
Demonstrable abilities with the coding best practices including testing, code review, and version control
CI/CD experience with some automation tooling like Jenkins, TravisCI, Github actions, etc.
Experience of data analysis – profiling, investigating, interpreting, and documenting data structures
Excellent teamworking, verbal, and written communication skills
Experience with Docker and Kubernetes