Transforming the scientific method through artificial intelligence and high-throughput automation for valuable therapeutic discovery and development across biological modalities.
Requirements
- Deep expertise in RL, including experience with policy optimization, value-based methods, or model-based RL
- Experience with distributed computing platforms (AWS, GCP, Azure, or on-prem clusters)
- Hands-on experience in multi-agent RL settings or hierarchical and offline RL methods
- Experience with online reinforcement learning in cost-sensitive settings
- Knowledge of LLM training/fine-tuning methods and experience with these methods at scale
- Experience with DPO, PPO, and/or RLHF for fine-tuning LLMs
- PhD in Computer Science, Machine Learning, Robotics, or a related quantitative field
Responsibilities
- Train and fine-tune cutting-edge models on scientific data
- Collaborate with experts across biology, materials science, and automation to push boundaries
- Implement robust evaluation frameworks, including custom benchmarks, to rigorously test model performance and reliability
- Incorporate RL approaches with large language models (LLMs) to enhance reasoning, planning, and decision-making capabilities
- Run rigorous experiments, document findings, and iteratively improve models based on quantitative results
- Develop and apply reinforcement learning methods to complex problems
- Work with distributed computing platforms (AWS, GCP, Azure, or on-prem clusters)
Other
- PhD in Computer Science, Machine Learning, Robotics, or a related quantitative field
- Demonstrated contributions to top-tier conferences (e.g., NeurIPS, ICML, ICLR, AAAI)
- Inclusive mindset and a diversity of thought
- Ability to work in unstructured and creative environments
- Passion for transforming science
- Commitment to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status
