Software Engineer - Backend
JasperSalary not specified
Full Time
Mid-level (3 to 4 years), Senior (5 to 8 years), Expert & Leadership (9+ years)
Software Developer (Backend), LiveDesign - Hyderabad at Schrödinger
Hyderabad, Telangana, India
Not SpecifiedCompensation
Junior (1 to 2 years), Mid-level (3 to 4 years)Experience Level
Full TimeJob Type
UnknownVisa
Drug Discovery, Materials Science, Life SciencesIndustries
Requirements
- Bachelor's/master's degree in computer science or equivalent stream with three to six years of experience in enterprise application development
- Practical understanding of CS concepts in the areas of data structures and algorithms, database management systems, operating systems, and computer networks
- Excellent programming skills, logical reasoning abilities, and enthusiasm for solving interesting problems, along with a willingness to learn
- Experience with event-driven microservices architecture and Kubernetes based deployments
- Enthusiasm for solving interesting problems and a willingness to learn new technologies
- Proficient interpersonal skills (oral/verbal communication), complemented by an ability to collaborate in a team environment
Responsibilities
- Design, build, and test high-performance, distributed components in problem areas, including but not limited to data aggregation/transformation/reporting and large-scale computations for a collaborative multi-user application
- Architect and implement scalable, maintainable solutions using technologies like Kafka and Kubernetes
- Contribute to a culture of clean code and continuous learning through regular code reviews
- Collaborate closely within a cross-functional, agile team composed of product designers, developers, and testers to deliver features and functionality that meet business and product goals
Skills
Kafka
Kubernetes
Event-Driven Architecture
Microservices
Data Structures
Algorithms
Database Management
Operating Systems
Computer Networks
Distributed Systems
Stream Processing
Schrödinger
Computational platform for biopharmaceutical research
About Schrödinger
Schrödinger provides a computational platform that aids in the research efforts of biopharmaceutical companies, academic institutions, and government laboratories around the world. Their platform offers advanced computational tools that help in drug discovery and development across various therapeutic areas. Schrödinger's products work by using sophisticated algorithms and simulations to predict how different compounds will interact, which can significantly speed up the research process. Unlike many competitors, Schrödinger not only licenses its software but also engages in collaborative research and drug discovery programs, allowing for a more integrated approach to scientific research. The company's goal is to enhance scientific research and development through its platform, ultimately leading to the discovery of new drugs and therapies.
New York City, New YorkHeadquarters
1990Year Founded
$362.7MTotal Funding
IPOCompany Stage
Government & Public Sector, Enterprise Software, BiotechnologyIndustries
501-1,000Employees
Benefits
Health Insurance
Dental Insurance
Vision Insurance
401(k) Company Match
401(k) Retirement Plan
Flexible Work Hours
Remote Work Options
Paid Vacation
Parental Leave
Risks
Recursion and Exscientia merger creates strong AI-driven drug discovery competitor.
Avicenna's independent expansion could lead to competitive tensions with Schrödinger.
High R&D costs may strain resources despite Gates Foundation's $10M grant.
Differentiation
Schrödinger's platform supports diverse clients, including biopharma, academia, and government labs.
The company excels in rapid drug discovery, exemplified by SGR-1505's swift development.
Schrödinger's collaboration with Avicenna enhances medicinal chemistry through machine learning.
Upsides
$19.5M Gates Foundation grant boosts platform for neglected diseases, expanding market reach.
Predictive toxicology tools initiative could lead to safer, efficient drug development.
SGR-1505 discovery showcases ability to reduce drug discovery timelines significantly.
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