Job description

About Us: At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs. Driven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms - Nace Data Intelligence and the Nace SLM Cloud - enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery. The work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth. If building a world-class AI team from the ground up excites you, we’d love to talk. About the Role We're looking for a Research Scientist to advance the core methods behind Nace AI's Small Language Models and metamodel framework. You'll work at the intersection of research and production, designing novel approaches to LLM adaptation, meta-learning, and hypernetworks, then carrying them from experimentation into systems that power Nace Data Intelligence and the Nace SLM Cloud. This is a hands-on role on a small, high-caliber team. You'll own research problems end to end: framing the question, running rigorous experiments, publishing or open-sourcing where it makes sense, and partnering closely with engineering to ship what works. Your work will directly shape how enterprises build customized, secure intelligence on our stack. Minimum Qualifications - 3+ years of combined research and engineering experience in machine learning, including hands-on work with language models.Direct experience working with Large Language Models (LLMs) or Vision-Language Models (VLMs) in research or production settings - Strong research background in Natural Language Processing, Machine Learning, or related disciplines with focus on language modeling - Proven track record in solving complex problems in language understanding, generation, or multimodal AI using rigorous quantitative methodologies - Demonstrated ability to clearly communicate research findings to diverse technical audiences - Proficient programming skills in Python and deep learning frameworks (PyTorch, JAX, or TensorFlow), with experience in distributed training and model optimization Preferred Qualifications - 5+ years of experience conducting applied ML research, with a sustained record of taking novel methods from experimentation to working systems. - PhD in Computer Science, Computational Linguistics, or closely related field with focus on language models and adaptive learning systems - Proven research and engineering experience with LLMs/VLMs, particularly in meta-learning or parameter-efficient adaptation, as evidenced by grants, fellowships, patents, internships, or contributions to open-source projects - First authored publications on language models, meta-learning, hypernetworks, or adaptive AI in recognized peer-reviewed conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) or journals - Kaggle experience is a plus. Preferred Technical Experience - Research expertise in LLM reasoning, hypernetworks, multi-task learning, meta-learning, designing novel LLM adaptation methods, Online Continual Learning

Requirements

Must-haves

Strong research background in Natural Language Processing, Machine Learning, or related disciplines with focus on language modeling
Proven track record in solving complex problems in language understanding, generation, or multimodal AI using rigorous quantitative methodologies
Demonstrated ability to clearly communicate research findings to diverse technical audiences
Proficient programming skills in Python and deep learning frameworks (PyTorch, JAX, or TensorFlow), with experience in distributed training and model optimization
5+ years of experience conducting applied ML research, with a sustained record of taking novel methods from experimentation to working systems.

Nice-to-haves

PhD in Computer Science, Computational Linguistics, or closely related field with focus on language models and adaptive learning systems
First authored publications on language models, meta-learning, hypernetworks, or adaptive AI in recognized peer-reviewed conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) or journals
Kaggle experience is a plus.
Preferred Technical Experience
Research expertise in LLM reasoning, hypernetworks, multi-task learning, meta-learning, designing novel LLM adaptation methods, Online Continual Learning

Application prep

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Keywords to mirror

aillmmachine learningpythonml

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