aipythonllmragpytorchreinforcement learningdistributed trainingsupervised fine tuninguncertainty estimation
Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Это четко очерченная исследовательская роль с заметным влиянием на LLM и доступом к большим вычислительным ресурсам в компании, создающей базовые модели. Зарплата не указана, поэтому оценить предложение относительно рыночного ориентира не получится.
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Зарплата не указанаСовременный стекФокус на ИИРесурсы для исследованийХорошие условия
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Hirify AI/Оригинал
TL;DR
(Senior) AI Researcher (LLM): Designing and scaling post-training methods for large language models, including supervised fine-tuning and reinforcement learning, to reduce hallucinations and improve evidence grounding with an accent on uncertainty estimation, calibration, and truthfulness evaluation. Focus on building distributed GPU training pipelines, benchmarking reasoning accuracy, and taking research projects from initial hypotheses to scalable production implementations.
Location: Hybrid in Heidelberg or Berlin, Germany
Company
hirify.global develops sovereign, human-centric AI and foundational models for governmental institutions and large enterprises, with applications in development, engineering, logistics, and manufacturing.
What you will do
Design, execute, and scale supervised fine-tuning and reinforcement learning methods to reduce LLM hallucinations and improve evidence grounding.
Research uncertainty estimation, calibration, self-consistency, and self-correction mechanisms that help models identify when they do not know.
Build and optimize high-throughput distributed fine-tuning and training pipelines across multi-node GPU clusters.
Design rigorous evaluations for hallucination rates, truthfulness, and reasoning accuracy.
Drive research projects independently from initial hypotheses through scalable production implementation and technical reporting.
Requirements
Hands-on experience fine-tuning or post-training large language models with modern distributed training frameworks.
Research or implementation experience in hallucination mitigation, truthfulness alignment, grounding in retrieval-augmented generation, uncertainty estimation, out-of-distribution detection, RAG evaluation, refusal calibration, or claim verification.
Strong programming skills in Python and PyTorch.
Availability for hybrid work in Heidelberg or Berlin, Germany.
Culture & Benefits
Work on foundational model research and next-generation AI methods with access to hundreds of GPUs.
30 days of paid vacation.
Flexible working hours and a hybrid working model.
Fitness and wellness offerings, including Wellhub, plus mental health support through nilo.health.
Subsidized company pension plan and Germany-wide transportation ticket.
Budget for additional technical equipment and a subsidized JobRad bike lease.
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