• This role offers an exciting opportunity to work at the forefront of AI, contributing to projects that push the boundaries of what’s possible with RL and LLMs
• Conduct advanced research in reinforcement learning, focusing on areas such as RLHF (Reinforcement Learning with Human Feedback), DPO (direct preference optimization), proximal policy optimization (PPO), and multi-agent systems
• Develop custom solutions for a variety of real-world practical applications and challenging problem domains
• Develop advanced reasoning models to enhance logical and contextual understanding, with applications in tasks such as code generation and structured decision-making
• Develop and fine-tune large language models, exploring post-training optimization techniques to enhance performance and efficiency
• Familiar with post-training data creation from either synthetic data processing or human annotated data pipelines
• Collaborate with cross-functional teams to integrate research findings into real-world applications and products
• Publish high-quality research papers in top-tier conferences and journals (e.g., NeurIPS, ICML, ICLR, ACL)

Stay up-to-date with the latest developments in AI, RL, and LLMs, identifying opportunities for innovation and improvement

• The ideal candidate will possess a strong academic foundation, practical experience with state-of-the-art algorithms for real-world applications, and expertise in data creation and curation for novel and challenging problem domains
• PhD in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field (or equivalent research experience)
• Strong expertise in large language models, including fine-tuning and post-training optimization techniques
• Proven track record of publishing in top-tier AI conferences or journals
• Proficiency in programming languages and frameworks commonly used in AI research, such as Python, TensorFlow, or PyTorch
• Excellent problem-solving skills, with a strong analytical and mathematical foundation

Ability to work both independently and collaboratively in a fast-paced research environment

Deep understanding of reinforcement learning algorithms, including recent advances like RLHF, DPO, PPO, and multi-agent systems

• The estimated base salary range for this role is $200,000 - $287,500
• Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan
• The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location

This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending & health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits