Software Engineer - Backend (Experienced, Remote/On-Site)
Sembly · Remote (Singapore)
Posted January 21, 1970
Job Description
As a backend software engineer (on-site or remote) you will be responsible for designing, developing, testing, and deploying high performance, efficient, and scalable systems, such as content systems, identity systems, reputation systems, consensus mechanisms, content or topic recommender systems, search systems, etc.
You might also help build and maintain common libraries and frameworks in our codebase.
If you choose to, you can also do full-stack development across our React.js-based frontend and Go backend. If you are interested in machine learning or data engineering, you can also choose to do software projects in the intersection of large scale systems and machine learning or data engineering.
You will work with some of our experienced engineers in the team who have many years of experience building large scale systems, and you will participate in product planning, design review, and code review process. You will also have the opportunity to shape our engineering standards, processes, and common libraries and tools.
Strong computer science fundamentals with a top tier educational background in Computer Science or other quantitative majors
Strong understanding of algorithms and data structures
Strong logic and abstraction ability, preferably with familiarity of how software works across the technology stack (network, memory, disk)
Experience with MVC libraries and microservice practices
Knowledge of a few programming languages, preferably Java, Go, Rust, or C++
Good engineering practices (design, coding, testing, tooling)
Good sense of ownership and a strong learning ability
We are an early stage stealth startup based in Singapore and the US backed by experienced entrepreneurs, engineers, and data scientists, aiming for creating a better human hive by restoring trustworthiness of information and uncovering hidden insights in public information and in teams and enterprises, using novel approaches involving machine learning, mechanism design, and behavioral design.
Originally posted on Himalayas
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