Senior ML Research Engineer, Post-Training for Personalization

What you'll do

• Develop novel algorithms for LLM post-training to advance personalized intelligence.
• Research post-training methodologies to help models adapt to individual user needs and personal context.
• Implement prompt optimization and reinforcement learning techniques for model improvement.
• Develop multimodal reasoning and context compaction techniques for efficient memory systems.
• Prototype and incorporate bleeding-edge algorithmic innovations into emerging agentic experiences.
• Interface with large-scale modeling and data infrastructure to deploy high-quality experiences.
• Translate emerging research into scalable, high-quality generative modeling technologies.

What we're looking for

• PhD in Electrical Engineering, Computer Science, or a related field with a focus on computer vision and/or machine learning.
• Comparable professional experience to a PhD in a relevant field.
• Strong fundamentals in machine learning and generative modeling.
• Hands-on experience in pre-training or post-training of large language models and reinforcement learning.
• Familiarity with distributed training.
• Proficiency in using ML toolkits such as PyTorch.
• Proven track record of research contributions through publications in top-tier conferences or open source contributions to algorithms.
• Experience with building and deploying Multimodal-LLMs and agentic systems with tool-use and memory capabilities (preferred).