LLM Post-Training Research Scientist (SFT/RLHF)

Scale AI

New York (NY)

Vor Ort

USD 181.000 - 226.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Benefits dieser Stelle

Health, dental and vision coverage
Retirement benefits
Learning & development stipend
Generous PTO
Commuter stipend

Zusammenfassung

Scale AI seeks Research Scientists and Research Engineers in New York to advance post-training methods for LLMs, including SFT and RLHF, across text and multimodal data. You will collaborate with researchers to refine data curation and evaluation practices and contribute to the next generation of generative models.

The role emphasizes rigorous analysis of model behavior, development of bias mitigation strategies, and publication of results in leading AI venues.

Qualifikationen

  • PhD or MSc in CS/ML or related field
  • Strong background in deep learning, RL, and large-scale model fine-tuning
  • Experience with post-training techniques such as RLHF
  • Excellent written and verbal communication
  • Published research at major ML conferences or journals
  • Previous customer-facing experience

Aufgaben

  • Research and develop post-training techniques to enhance LLMs in text and multimodal modalities
  • Design and experiment new approaches to preference optimization
  • Analyze model behavior, identify weaknesses, and propose bias mitigation and robustness improvements
  • Publish research findings in top-tier AI conferences

Kenntnisse

SFT
RLHF
Reward modeling
Bias mitigation
Model evaluation
Publications
Communication
Customer facing

Ausbildung

PhD or MSc in CS/ML

Jobbeschreibung

Scale AI seeks Research Scientists and Research Engineers in New York to advance post-training methods for LLMs, including SFT and RLHF, across text and multimodal data. You will collaborate with researchers to refine data curation and evaluation practices and contribute to the next generation of generative models.

The role emphasizes rigorous analysis of model behavior, development of bias mitigation strategies, and publication of results in leading AI venues.

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