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 [email protected]

Job Tags

Similar Jobs