PhD Research Intern

About the Role

We’re looking for smart, motivated, and curious PhD students to join us next summer or earlier as Research Scientist Interns. This is a unique opportunity to explore new research directions in AI, reinforcement learning, and multi-agent systems while working alongside a small, elite team of researchers and engineers.

You will not be constrained to our current product goals, but rather, you will be given the resources needed to accomplish your best work. You will be encouraged to publish your work with us in conferences/journals.

Qualifications

• Currently pursuing a PhD in Computer Science (AI/ML, NLP, Robotics, etc.), or computational fields in Physics, Chemistry, Biology, Neuroscience, Cognitive Science, etc.

• Demonstrated ability to design, implement, and evaluate experiments

• Strong publication record or evidence of impactful research contributions

• Startup-oriented mindset: excited by ambiguity, rapid iteration, and big ideas

Bonus

• Publications at top conferences (ICML, ICLR, Neurips, UAI, RSS, CoRL, JMLR, TMLR etc)

• Experience with large-scale distributed training or inference engines (e.g., DeepSpeed, vLLM, FSDP)

• Contributions to open-source projects in AI/ML or systems research

• Cross-disciplinary interests (e.g., neuroscience, human-computer interaction, generative media)

What makes us interesting

• Small, elite team of ex-founders, researchers from top AI Labs, top CS grads, and engineers from top companies

• True ownership You will not be blocked by bureaucracy, shipping meaningful work within weeks rather than months

• Serious momentum We're well-funded by top investors, moving fast, and focused on execution

What we do

• Ship consumer products powered by cutting-edge AI research, and

• Build infrastructure that facilitates research and product, and

• Innovate cutting-edge research that will open up new consumer product forms

The Details

• Minimum 40 hours a week, onsite in Menlo Park (start and end date flexible)

• Startup hours apply

• Generous compensation