Role Description
Where multiple locations are listed for this role, the position may be based in any of those locations, with priority determined according to the order of listing.
**What You’ll Do**
As a PhD intern, you will:
* Collaborate with research scientists to advance methods in:
+ Planning and RL for computer use (e.g. behavioral cloning, RL on model weights, RAG-based domain knowledge)
+ Multimodal grounding (e.g. vision-only models, tree search, hybrid methods with large models)
+ Reward/judge modeling (e.g. error analysis, human evaluation, training judge models)
+ User intent understanding (e.g. modeling vague queries, preference learning)
* Contribute to building datasets, running experiments, and benchmarking results
* Explore novel approaches and help derisk Simular’s long-term technical roadmap
* Document and communicate findings through internal reports or academic-style writing
**You might be a fit if**
* Currently pursuing a PhD in Computer Science, Machine Learning, or related field
* Research background in at least one of: Reinforcement learning, Large language/vision-language models, Computer vision and multimodal perception, Representation learning
* Experience conducting experiments and publishing or preparing papers in top-tier conferences (NeurIPS, ICLR, ICML, CVPR, ACL, etc.)
* Strong coding and prototyping skills in Python and ML frameworks (PyTorch/JAX)
* Curiosity, initiative, and interest in bridging fundamental research with applied AI