Première détection par notre source: il y a 6 jours
Research Scientist - Toronto, Multiple Openings
@ Skyfall AI
Toronto, Ontario
En bref
Skyfall AI is hiring a Research Scientist in Toronto to advance agentic systems. The role develops new methods for reasoning, planning, reinforcement learning with LLMs and multi-agent systems. The posting is a full-time position with multiple openings.
Responsabilités :
- Develop techniques for reinforcement learning with LLMs, planning and multi-agent systems
- Conduct experiments and build datasets to improve LLM performance
- Stay current on academic research in planning, reasoning and RL
Résumé des exigences :
- PhD in CS, ML or AI preferred, or Master's plus 5+ years industry research
- Experience with LLM technologies such as GPT and Llama
- Experience with RL techniques such as REINFORCE and PPO
- Experience with PyTorch or TensorFlow
Outils et compétences
Source et dates
Type de source non vérifié · dover
- Date publiée par l’employeur
- Non fourni
- Première détection par notre source
- 2026-11-04
- Mise à jour du dossier source
- 2026-11-04
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Données et analytique |
| Heures par semaine | 40 (estimé) |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
| Formation | Doctorat (préféré), Maîtrise (exigé) |
| Domaines d’études | Computer Science, Machine Learning, Artificial Intelligence |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Skyfall AI
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 4 |
Research Scientist at Skyfall AI
Job Overview:
As an applied research scientist at Skyfall you will be responsible for advancing the state of the art for agentic systems. As part of your work you will develop new methods and techniques fo reasoning and planning with Large Language Models (LLMs), multiagent systems, and model based reinforcement learning to help reach Skyfall’s goal of developing a fully autonomous enterprise. We are seeking individuals who are passionate about areas such as program synthesis, natural language processing, conversational systems and reinforcement learning.
Research Key Responsibilities:
Develop new techniques and methods for advancing the state of the art in the areas of reinforcement learning with LLM , planning and reasoning with LLM and multi agent systems
Develop methods and techniques for advancing the state of the art for multi agent agentic systems
Conduct experiments and build datasets that improve LLM performance in complex, context-dependent workflow
Stay up to date on the latest advancements in academic research related to planning/reasoning with LLM , Reinforcement Learning and Multi Agent Systems
Qualification
PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field (preferred) or Masters in Computer Science , Machine Learning , Artificial Intelligence or related field and at least 5+ years of industry research experience
Experience with large models, a strong passion for the field, familiarity with large model technology development, and an understanding of the latest advancements in the domain.
Experience with large language model technologies such as Open AI GPT , Llama etc.
Experience with RL techniques such as Reinforce, PPO etc
Experience with neural network frameworks such as Pytorch , Tensorflow et
Preferred Skills & Experience:
Experience developing methods and techniques for planning with LLMs
Expertise in Reasoning with LLMs to address nuanced decision-making processes required in production models
Experience with multi agent reinforcement learning or multi agent planning
Preference for candidates with publications in well-known journals, blogs, or open-source projects in the fields of large language models, planning and reasoning with LLMs, multi-age reinforcement learning, or model based reinforcement learning
Location - Toronto
Journal
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