Job Description
⚡ Research Scientist - Data focus
💊 Foundation Models, AI Research Institute
🌎 San Francisco Bay Area, USA
💸 $200,000 - $350,000 salary + bonus
Come join a revolutionary AI research lab in SF Bay Area that is poised to develop & publish high-impact breakthroughs in GenAI - across LLMs and Multimodal AI.
As part of the team, you’ll work at the intersection of data, large-scale training, and foundation model innovation. You will collaborate with world-class researchers, data scientists, and engineers to solve critical challenges in creating robust, scalable, and reasoning-capable LLMs. Your research will shape the way data is curated, processed, and leveraged to train the next generation of intelligent systems.
Responsibilities:
- Lead research on data-centric approaches for LLMs , including pretraining corpus design, data valuation, and speculative decoding strategies.
- Develop pipelines to process challenging data sources into structured and reproducible training datasets.
- Build and optimize agentic data pipelines , integrating retrieval, self-curation, and multi-agent feedback for high-quality training and evaluation data.
- Collaborate with researchers on alignment and reasoning-focused training that leverage data-driven approaches for improving LLM capabilities.
- Prototype and deploy evaluation frameworks to measure data quality, coverage, and downstream impact on LLM reasoning.
- Publish findings at top-tier venues (e.g., NeurIPS, ICLR, ACL, EMNLP) and represent the institute at international conferences.
- Contribute to open-source tools, datasets, and benchmarks that advance the global foundation model research community.
Requirements:
- Master’s degree in Computer Science, Data Science, or a related technical field (PhD strongly preferred)
- Experience collecting and curating high-quality text data including multi-lingual data.
- Hands-on experience with large-scale dataset curation and preprocessing for ML/LLM training.
- Prior works synthesizing complex datasets. Code, math, and agentic data are higher priority
- Experience with ML infrastructure for scalable training, evaluation, and debugging .
- Experience at the intersection of data and post-training (RL/SFT)
- Proven ability to independently drive research questions related to data quality, scaling, or reasoning .
Preferred Experience:
- Experience with retrieval-augmented generation (RAG) , agentic data pipelines, or reasoning benchmarks.
- Contributions to speculative decoding, self-curation, or reinforcement learning from synthetic data .
- Background in knowledge graphs, semantic search, or indexing systems .
- Strong publication record in leading AI conferences.
- Prior contributions to open-source ML data tools or benchmarks .
- Prior work on speculative decoding/contributions to LLM serving engines
- Prior work on training LLM-as-a-judge
- Deep expertise with tokenization/training tokenizers
Why apply:
- Opportunity to build out a new division at the forefront of AI innovation
- FAANG competitive salary & package
- Work alongside superstars from FAANG labs & leading AI companies
- Medical, Dental and Vision Insurance
- Relocation package available
🌎 San Francisco Bay Area, USA
📧 Interested in applying? Please click on the ‘Easy Apply’ button or alternatively email me your resume at stefani.lukic@storm3.com
Job Tags
Relocation package,