What are the responsibilities and job description for the Full Stack Software Engineer (data-centric AI) position at International Staff Consulting?
Our client is focused on data-centric AI (DCAI), providing algorithms / interfaces to help companies (across all industries) improve the quality of their datasets and diagnose / fix various issues in them. Our client develops next-generation DCAI algorithms that we release publicly as open-source software (github.com / cleanlab / cleanlab) as well as enterprise SaaS products with interfaces for data scientists / engineers to effectively improve their data quality and produce more reliable ML models.
Pioneer novel software systems for the rapidly growing field of data-centric AI. Our tools enable data scientists and engineers across all industries to effectively diagnose and fix issues in their datasets, thus improving the quality of their business's core asset.
Work across the whole stack, from UI to backend, end-to-end, building highly scalable and distributed solutions.
Develop new features and infrastructure to support our rapidly emerging business and project requirements.
Work on interesting challenges - novel interfaces for data visualization / interpretation and editing raw data directly - using a modern tech stack at a dynamic startup operating in one of the fastest-growing subfields of data science & AI.
Responsibilities :
- Work closely with designers, cloud engineers, UI / UX designers, and product managers to implement full-stack solutions.
- Deliver innovative, engaging web applications using the latest in both front-end and back-end technologies.
- Collaborate with other engineers to build streamlined systems and help establish a strong engineering culture across the company.
Qualifications
Why is This a Great Opportunity?
About the team
The founders at this dynamic tech start-up (3 ML PhDs from MIT) worked at OpenAI, Google, Microsoft, Amazon, AWS, Facebook AI Research (FAIR), Dropbox, Oculus, Palantir, NASA, General Electric, MIT Lincoln Laboratory, MIT, Harvard, and Stanford - at every place they worked, they repeatedly encountered the same issue - AI solutions failed to work reliably on real-world, human-centric data due to label errors and poor data quality. So, they spent eight years of PhD research at MIT inventing a new field to solve this problem and after successful pilots with world-leading organizations, and this well-funded tech startup emerged.
Benefits
