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Research Lead / Principal Scientist & Manager Post-Training (AI)
Мэтч & Сопровод
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Описание вакансии
Текст:
TL;DR
Research Lead / Principal Scientist & Manager Post-Training (AI): Designing and leading the research strategy for transforming foundation models into reliable, domain-specific systems with an accent on RLHF, preference optimization, and long-horizon reasoning. Focus on grounding reinforcement learning in physical laws and CAD kernels to ensure robustness in high-precision engineering domains.
Location: Remote (US, Canada, EU) or Toronto, ON, Canada
Company
AI Lab advances state-of-the-art research across generative AI, multimodal foundation models, and reasoning systems to impact industries that shape the physical world.
What you will do
- Own post-training strategy for model development, covering RLHF, preference optimization, agentic systems, and long-horizon reasoning.
- Develop novel algorithms to improve model reliability, controllability, and alignment.
- Manage and mentor a growing team of AI scientists, setting technical direction and research priorities.
- Design evaluation frameworks for tool use, agentic behavior, and real-world workflow completion.
- Partner with infrastructure teams to build scalable and reproducible post-training workflows.
- Contribute to publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR, SIGGRAPH) and patents.
Requirements
- Deep hands-on expertise in reinforcement learning for foundation models (RLHF, RLAIF, DPO, PPO).
- Proven experience leading or mentoring technical research teams in academia or industry.
- PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field.
- Strong intuition for model behavior, alignment challenges, and post-training trade-offs.
- Ability to communicate complex technical trade-offs to both technical and non-technical audiences.
- Must be based in the US, Canada, or the EU.
Nice to have
- Experience at a frontier model lab or advanced applied AI organization.
- Strong publication record at leading ML or AI venues.
- Background in alignment research, preference learning, or agentic AI.
- Experience deploying or supporting production AI systems.
Culture & Benefits
- Collaborative environment with a direct line from research advances to product impact at scale.
- Access to unique, domain-grounded verifiers based on physics simulation and CAD kernels.
- Inclusive culture committed to diversity and equal opportunity.
- Competitive compensation package including annual cash bonuses and stock grants.
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