Machine Learning Researchers; Reinforcement Learning - Level

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method. We aim to solve humankind's challenges in human health, climate, and sustainability at a pace and scale never experienced before. Learn more a.ai

Location:

Cambridge, MA

We are uniquely cross-functional and collaborative. We actively reimagine how teams work together and communicate. We seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments where all voices are heard because experience comes in many forms, skills are transferable, and passion goes a long way.

Lila Sciences is seeking experienced, creative, and talented Machine Learning Researchers (Reinforcement Learning) across Scientist, Senior Scientist, and Principal Scientist levels. Title will be determined by merit and experience level.

• Join our agile team to reimagine the way scientific research is conducted. You ll train and fine-tune cutting-edge models on scientific data. Collaborate with experts across biology, materials science, and automation to push boundaries.
• Incorporate RL approaches with large language models (LLMs) to enhance reasoning, planning, and decision-making capabilities.
• DPO, PPO, and/or RLHF for fine-tuning LLMs
• Implement robust evaluation frameworks, including custom benchmarks, to rigorously test model performance and reliability.

• PhD in Computer Science, Machine Learning, Robotics, or a related quantitative field, with demonstrated contributions to top-tier conferences (e.g., NeurIPS, ICML, ICLR, AAAI).
• 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).
• Demonstrated ability to run rigorous experiments, document findings, and iteratively improve models based on quantitative results.
• 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.

Lila Sciences is committed 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.

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.