4h ago

Research Staff, LLMs

SpainRemoteFull-timelead

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Research Staff, LLMs based in Spain.

Technical Tools

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We are seeking an experienced AI researcher specializing in large language models (LLMs) and advanced transformer architectures to help push the boundaries of next-generation AI. In this role, you will investigate fundamental challenges in model architecture, data curation, distributed training, and reinforcement learning. You will design and execute ambitious research programs, translating scientific discoveries into scalable models and production-ready capabilities. Working alongside a highly technical research team, you will explore new approaches to complex problems and rapidly validate promising ideas through experimentation. The environment is highly innovative, AI-first, and fast-moving, with significant autonomy and opportunities to shape research direction. Your work will contribute to making advanced AI systems more capable, efficient, and accessible, particularly across voice-based applications.

You will contribute to cutting-edge LLM research, combining scientific investigation with hands-on engineering to develop, optimize, and deploy advanced models.

Collaborate with other research scientists to identify, define, and develop new research initiatives focused on large language models.
Conduct comprehensive literature reviews, evaluating, classifying, and synthesizing existing research methods and emerging techniques.
Design, plan, and execute experimental programs to investigate new approaches to LLM development and optimization.
Research and improve transformer architectures, including model design, training methods, and performance optimization.
Lead large-scale LLM training initiatives using distributed computing infrastructure.
Investigate and improve data curation strategies, training efficiency, model optimization, and reinforcement learning techniques.
Translate successful research experiments and proofs of concept into scalable solutions and production-ready models.
Contribute to the development and deployment of new models, ensuring that research outcomes can be integrated into real-world products.
Document research findings and communicate complex technical concepts clearly to relevant audiences.
Monitor the latest developments in deep learning and LLM research, identifying techniques with potential applications to products and future research directions.
Leverage AI tools to automate workflows, accelerate experimentation, and increase research productivity.

Requirements

~2 min read

The ideal candidate combines strong applied deep learning research experience with practical expertise in building, training, and optimizing large-scale language models.

At least 3 years of experience in applied deep learning research.
Strong understanding of neural network architectures, their applications, and the implications of different model types and loss functions.
Proven hands-on experience with large language models, including data curation, distributed large-scale training, transformer architecture optimization, and reinforcement learning.
Strong programming skills in Python and practical experience with PyTorch.
Experience working with different transformer architectures, including autoregressive and sequence-to-sequence models.
Practical knowledge of distributed computing and large-scale data processing.
Experience designing and conducting research experiments, analyzing results, and using findings to improve model performance.
Strong analytical and problem-solving skills, with the ability to investigate complex technical challenges.
Excellent communication skills and the ability to explain sophisticated research concepts clearly.
An innovative, self-directed mindset, with the ability to work autonomously and rapidly test new ideas.
Strong interest in state-of-the-art AI research and translating scientific advances into practical applications.
Comfort working in a fast-paced, AI-first environment that requires continuous learning, experimentation, and adaptation.

Deep understanding of transformer architectures, causal language models, and their underlying mechanisms.
Experience with distributed training and distributed inference approaches for LLMs.
Familiarity with reinforcement learning from human feedback (RLHF), including labeling and training pipelines.
Up-to-date knowledge of emerging LLM techniques, architectures, and research developments.
Published research papers in deep learning, particularly in large language models and deep neural networks.