Lila Sciences is seeking to automate never-ending innovative processes of discovery and exploration through the development of open-ended machine learning systems.
Requirements
- Expertise in ML frameworks (PyTorch/TensorFlow/Jax)
- QD algorithm implementation
- Neuroevolution algorithm implementation
- Experience in training and deploying ML models on distributed computing services (e.g. AWS/GCP/Azure, or clusters)
- Generative models (e.g. LLMs, diffusion models, multimodal models)
- Pre-training, fine-tuning, RLHF, distillation, mechanistic interpretability, and quality diversity (QD) techniques
Responsibilities
- Designing, implementing, and modifying generative 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
- Quality diversity (QD) algorithms like MAP-Elites, novelty search with local competition, POET, OMNI, minimal criterion novelty search, etc.
Other
- 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
- Inclusive mindset and a diversity of thought
- Ability to work in unstructured and creative environments
