Job Details

Overview

Machine Learning Scientist, Open-Endedness (Level Flexible).

About Lila Sciences: 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, solving humankind's greatest challenges in human health, climate, and sustainability at a pace and scale never experienced before. Learn more at www.lila.ai.

At Lila, we are uniquely cross-functional and collaborative. 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 and 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.

Your Impact at Lila

Lila Sciences is seeking experienced, creative, and talented Machine Learning Scientist (Open-Endedness) across Scientist I/II and Senior Scientist levels. Title will be determined by merit and experience level. Open-Endedness is an emerging area of machine learning that aims to automate never-ending innovative processes of discovery and exploration. The Open-Endedness Team, led by Ken Stanley, investigates how a continual chain of deep transformative creativity can be maintained that far exceeds the derivative creativity seen in current models. In effect, the systems developed on this team will go beyond simply solving problems posed by users, to conceiving the future unimagined directions of science itself.

To realize this vision, we're seeking a broad tapestry of ML expertise to facilitate daring and unconventional investigations, including but not exclusive to pre-training, fine-tuning, RLHF, distillation, mechanistic interpretability, and quality diversity (QD) techniques.

What You'll Be Building

• Designing, implementing, and modifying generative models (e.g., LLMs, diffusion models, multimodal models) through unconventional pipelines to achieve unconventional behaviors
• Unconventional evaluation techniques, including subjective evaluation and the evaluation of interestingness
• Creative approaches to investigating, understanding, and visualizing the internal representations of large models, encompassing mechanistic interpretability but also going in new directions beyond it
• Quality diversity (QD) algorithms like MAP-Elites, novelty search with local competition, POET, OMNI, minimal criterion novelty search, etc., with LLMs or other large models potentially being updated on the inner loop

What You'll Need to Succeed

• PhD in quantitative disciplines ideal, but will consider self-taught researchers with exceptional achievements
• Publications in relevant conferences, such as NeurIPS, ICML, AAAI, ICLR, GECCO, ICCC
• Expertise in ML frameworks (PyTorch/TensorFlow/Jax); and/or QD algorithm implementation; and/or neuroevolution algorithm implementation
• Experience in training and deploying ML models on distributed computing services (e.g., AWS/GCP/Azure, or clusters).

We're All In

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.

A Note to Agencies

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 strictly prohibited unless contacted directly by Lila Sciences' 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.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Other

Industries

• Technology, Information and Internet

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