Software Engineering LMTS
Role details
Job location
Tech stack
Job description
We are looking for Members of Technical Staff with strong academic backgrounds to join our team and lead research initiatives on training foundation models for a brand new enterprise agent. You will work with a small team of hands-on researchers - equipped with substantial GPU resources - to advance the state-of-the-art in multi-modal vision-language models, reinforcement learning, and action models. You will develop novel architectures and training approaches while publishing your findings in top-tier AI conferences and journals. Responsibilities Your role will span both theoretical research and practical implementation:
- Designing and implementing novel supervised fine-tuning, preference learning and reinforcement learning techniques
- Developing innovative methods for data curation, including synthetic data generation pipelines and optimal dataset composition strategies
- Conducting rigorous experimentation to optimize model performance through data mixes, regularization techniques, and low-level optimizations
- Advancing the theoretical understanding of transformer architectures and their applications to multi-modal learning
- Publishing research findings in top-tier venues such as NeurIPS, ICML, and ICLR
Requirements
Do you have a Doctoral degree?, * Deep knowledge of transformer architectures and vision-language models and strong theoretical understanding of deep learning fundamentals
- Expertise in training and fine-tuning open source models using techniques such as supervised fine-tuning or reinforcement learning
- Proficiency in PyTorch and related frameworks
- Experience with large-scale distributed training and inference using e.g. DeepSpeed, FSDP, Ray
Bonus Qualifications
- PhD in Computer Science, Machine Learning, or related field
- Strong publication record in top-tier ML conferences (NeurIPS, ICML, ICLR) or top journals
- Research experience at leading academic or industrial research labs
- Demonstrated expertise in reinforcement learning
- Contributions to open-source ML frameworks
- Experience developing novel datasets or data generation approaches
- Background in causal reasoning or alignment research
