Job description

In this research scientist role you will join a small team of researchers performing fundamental research investigating foundation models for scientific domains. You will be involved in all aspects of performing research including project definition, method development, and experimental design. You will also run your own experiments, then analyze and interpret the results. You will take an active role in writing papers for publication and also using our findings to solve applied problems. You will also have the opportunity to collaborate with partner teams across Apple. PhD in computer science, statistics, physics, chemistry, electrical engineering, or operations research. Other hard sciences may also be considered. 3 publications in top-tier machine learning, statistics, or natural language processing venues. Deep knowledge of foundation models and experience training them and applying them to real, complex datasets as demonstrated through publications or code repositories. Experience designing experiments to understand how foundation models work. Knowledge of Bayesian statistical methods and how they are used for scientific inference. Experience with large language models and – both training them and using tools like vllm for inference. Proficient implementing ML models and experiments in Python and Pytorch/Jax. Familiarity with interpretability methods like activation patching/causal tracing. Demonstrate the ability to refine ambiguous research ideas to construct a coherent and logically sound story.

Requirements

Must-haves

PhD in computer science, statistics, physics, chemistry, electrical engineering, or operations research. Other hard sciences may also be considered.
3 publications in top-tier machine learning, statistics, or natural language processing venues.
Deep knowledge of foundation models and experience training them and applying them to real, complex datasets as demonstrated through publications or code repositories.
Experience designing experiments to understand how foundation models work.
Knowledge of Bayesian statistical methods and how they are used for scientific inference.
Experience with large language models and – both training them and using tools like vllm for inference.
Proficient implementing ML models and experiments in Python and Pytorch/Jax.
Familiarity with interpretability methods like activation patching/causal tracing.
Demonstrate the ability to refine ambiguous research ideas to construct a coherent and logically sound story.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpython

Platform: apple
Effort: medium