Lila Sciences, Inc. | Cambridge, MA Machine Learning Researchers (Reinforcement Learning) - Ope[...] - Flagship Pioneering : Job Details

Lila Sciences, Inc. | Cambridge, MA Machine Learning Researchers (Reinforcement Learning) - Ope[...]

Flagship Pioneering

Job Location : Cambridge,MA, USA

Posted on : 2025-08-06T01:16:42Z

Job Description :

Cambridge, MA USA

Company Summary

Lila Sciences is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. Lila Sciences is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results. Join our mission-driven team and contribute to the future of science.

Our Life Sciences effort is leveraging AI and high-throughput automation for valuable therapeutic discovery and development across biological modalities.

At Lila Sciences, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you'd love to work in, even if you only have some of the experience listed below, please apply.

The Role

Lila Sciences is seeking experienced, creative, and talented Machine Learning Researchers (Reinforcement Learning) across Scientist, Senior Scientist, and Principal Scientist levels to join our team. 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. We're looking for an ML pro skilled in reinforcement learning and software engineering excellence. Ready to transform science? Let's talk!

Candidates should have experience and/or interest in:

  • Incorporating RL approaches with large language models (LLMs) to enhance reasoning, planning, and decision-making capabilities.
  • DPO, PPO, and/or RLHF for fine-tuning LLMs
  • Implementing robust evaluation frameworks, including custom benchmarks, to rigorously test model performance and reliability.

Qualifications:

  • 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.

Ideal:

  • 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.

Working at Lila Sciences, you would have access to advanced technology in the areas of:

  • AI experimental design and simulation
  • Automated liquid handling and instrumentation

Flagship Pioneering 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.

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

#J-18808-Ljbffr
Apply Now!

Similar Jobs ( 0)