Job Description
Cleanlab’s hacker in residence role blends developer relations/advocacy work with building awesome Data-Centric AI applications and sharing them with the community. You’ll help AI engineers learn about data-centric AI, LLMs, RAG, Agents and how Cleanlab and other revolutionary technologies can help them solve their Data/AI challenges.
In this dynamic role, you’ll get to write open-source code, publish technical blog posts, make videos and social media content, host events/hackathons, engage in technical discussions in in-person and online forums/conferences, participate in vibrant AI communities, and help identify developer needs.
This role is a mix of data science, software engineering, technical writing, producing/launching content, community building, and engagement. This role is ideal for a multi-talented individual who likes wearing many hats and doing work that deeply engages technical audiences.
We’re looking for someone who has expertise around the cutting-edge of LLMs/RAG/Agents, excellent technical writing skills, and a deep love and understanding of people and communities. Good candidates for this role will already: be active on Github and social media, have a blog, have attended many conferences/hackathons, and know about the latest trends in Data/AI.
Working at Cleanlab is awesome! Beyond the opportunity to work at a well-funded AI startup with an incredible, friendly founding team of MIT graduates, all full-time employees receive the following:
The compensation range for this role is $140,000 to $170,000. The final offer details are determined by several factors including candidate experience/expertise and may vary from the pay range provided.
Prior to Cleanlab, our founders (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 we worked we 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, we spent eight years of PhD research at MIT inventing a new field to solve this problem and after successful pilots with world-leading organizations, Cleanlab emerged.
Everything we do at Cleanlab is guided by our north star – to improve the world’s ML data more easily and quicker than any other solution – enabling AI systems to train more reliably on real-world, messy, error-prone data. We develop next-generation data-centric AI, open-source algorithms and provide no-code SaaS enterprise solutions to help individuals and teams at companies (across all industries) diagnose/fix issues in their datasets and produce more reliable ML models by providing clean labels for training.
While many companies can help store/manage data or develop ML models, there exist few solutions today to improve the quality of existing data, which is the core asset of the modern enterprise. This is where you come in. At Cleanlab, you’ll be able to take ownership of critical projects that pioneer the future of data-centric AI.
We are a hybrid company, with over half of our team (and office) located in San Francisco.
Cleanlab develops a data-centric artificial intelligence (DCAI) to help companies improve the quality of their datasets. It handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks.
