Applied AI Researcher - Foundation Models
Actively Hiring
Full-time Posted 12 days ago
Responsibilities
- check_circle design and conduct research on Large Language Model pre-training
- check_circle design experiments, implement them in code, run them at scale, and analyze their results
- check_circle investigate model architectures, optimization strategies, training dynamics, and scaling behavior
- check_circle research and develop multilingual training strategies
- check_circle work on data selection, quality, composition, and data mixture experiments for LLM pre-training
- check_circle evaluate models across multilingual and general-purpose benchmarks
- check_circle monitor and benchmark the state of the art in Large Language Models
- check_circle run experiments on large-scale GPU and HPC infrastructure
- check_circle translate research hypotheses into measurable experiments and actionable decisions for large-scale training
Basic qualifications
- 3+ years of research/industry experience in a relevant area of Deep Learning, Machine Learning, Natural Language Processing, or Large Language Models
- strong understanding of modern deep learning and Transformer-based language models
- excellent programming skills in Python
- experience designing and running machine learning experiments
- familiarity with GPU-based training environments and Unix/Linux systems
- ability to analyze experimental results and make research decisions based on empirical evidence
- interest in large-scale experimental research and language model pre-training
- ability to follow, understand, and reproduce recent scientific literature
- excellent written and spoken English
- ability to work effectively with both researchers and engineers
- you have direct experience pre-training or continuing the pre-training of Large Language Models
- you have experience with distributed and multi-GPU training
- you have worked with large-scale training frameworks such as Megatron Bridge
- you have experience optimizing GPU utilization, throughput, memory consumption, or large-scale training stability
- you have worked on multilingual NLP or multilingual language models
- you have experience with large-scale dataset curation, filtering, deduplication, or data mixture design
- you have experience with LLM evaluation and benchmarking
- you have experience working with HPC environments
About the company
- check_circle large-scale language model pre-training
- check_circle multilingual language modeling
- check_circle data curation, filtering, scoring, and mixture design
- check_circle scaling laws and training dynamics
- check_circle model architecture and optimization
- check_circle distributed and multi-GPU training
- check_circle evaluation and benchmarking of Large Language Models
- check_circle continued pre-training and mid-training strategies
Tags & Focus Areas
Fulltime Remote Ai
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