Machine Learning Engineer (Masters/PhD)

Note: This post is for our recruiting events at selected universities only (Stanford, UT Austin). For general openings, please visit our Careers page.

Born from the understanding that AI deployment shouldn't require months of preparation or compromise on quality, we've built a comprehensive platform that turns your brand values into production-ready AI applications in days, not months.

Our product is AI Judges that are trained with proprietary auto-align technology and powered by state-of-the-art research on Alignment and RL. We help companies build AI systems that aren't just safe and reliable, but truly aligned with their brand values and business objectives.

Backed by top-tier Silicon Valley venture capital firms, we're on a mission to make safe, reliable, and highly-performant frontier AI for enterprise use-cases a reality.

Join us in pushing the boundaries of what's possible in AI! Learn more about the company here.

As a Research / ML Engineer, you will play a crucial role in conducting and enabling cutting-edge research and translating it into our core product pipeline. You will work closely with other members of the technical staff to develop and improve state-of-the-art judges for safety, reliability, and data curation. Your technical skills will accelerate our research and ensure that our product remains at the forefront of innovation.

There are a few specific things we’ll be looking for that will help you succeed in this role:

• PhD or Masters in Computer Science with an emphasis on Machine Learning, or a related technical field

• 3-4 years of experience in an industry research lab or equivalent academic experience

• Strong background in machine learning systems, such as distributed training of large models and/or ML performance optimization

• Knowledge of ML/AI applications and models, especially foundation models, how they are constructed, and how they are used

• Experience contributing to research communities, including open-source research projects or publishing at conferences (e.g., CVPR, NeurIPS, ICCV/ECCV, BMVC)

• Strong foundations in software engineering and empirical research

• Ability to work separately and as part of a team. Excellent communication and presentation skills

We are working leading global enterprises to deliver cutting-edge AI safety and reliability tools. And we are looking for brilliant, high-agency, low-ego rockstars to join us on this crusade. We want the best of the best and firmly believe greatness begets greatness.
Come here to push yourself hard, learn things fast, experience unmatched excellence, and do your life's work.