We're looking for a high performance ML-focused software engineer to join us on our mission to build AI superpowers for developers. We serve one of the largest scale and most demanding LLM applications in the world.
About Codeium
Featured on the Forbes AI 50 list, Codeium has risen to become a leader in the AI developer tools space in just over a year, giving hundreds of thousands of users around the world code autocomplete, in-editor chat assistants, and more. our IDE extensions span 70+ programming languages and 40+ editors. Our state-of-the-art proprietary language models and custom inference stack allow us to deliver the best experience possible to our users. We've achieved substantial revenue and enterprise traction as proof of the quality and usefulness of our tools.
We're one of the fastest growing AI startups, focused on product, revenue, and customer experience. We work hard, and we operate with a high degree of trust, agency, and ownership.
What you'll do
Develop custom LLM serving systems and corresponding datacenter infrastructure to deliver high model quality at very low latency and cost.
Improve our LLM training software both in terms of model architecture changes that improve model quality or increase iteration speed and reliability.
Build indexing systems capable of serving queries instantly from many terabytes of data within customer deployments.
Contribute to infrastructure for petabyte-scale data pipelines to accelerate our ML research work.
About you
Strong software engineering skills. There are no pure research scientists at the company.
Excellent quantitative, analytical and estimation skills.
Strong grasp of computer and networking architecture, particularly with GPU hardware and HPC networks.
Familiarity with AI-powered developer tools like Codeium, Copilot, ChatGPT, and others is a strong plus.
What we believe
Our best work is done in person. The team goes in 5 days a week into our office in downtown Mountain View, CA (within walking distance of the Caltrain station).
Research is in service of a better product. While we read many papers, we won't have time to write them. The best AI researchers have excellent software engineering skills and know that infrastructure and evaluation work are critical.
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