Apple Verified
Consumer Electronics, Software, Services, Retail
AIML - Sr Machine Learning Research Scientist, Data and ML Innovation
Seattle, WAOnsiteFull TimeSeniorPosted 2 months agoVisa sponsorship available
Required skills
- statistics
- physics
- chemistry
- electrical engineering
- operations research
- hard sciences
- publications
- machine learning
- natural language processing
- foundation models
Nice to have
- large language models
- training
- vllm
- inference
- Python
- Pytorch
Role summary
This role is for a Senior Machine Learning Research Scientist focused on understanding and expanding the capabilities of foundation models in scientific domains. The scientist will conduct fundamental research, define projects, develop methods, design experiments, analyze results, and publish findings. The position involves applying research to solve practical problems and collaborating with internal teams. A PhD in a relevant quantitative field and a strong publication record in top-tier ML/stats/NLP venues are required, along with deep knowledge of foundation models and experience with Bayesian statistics. Preferred qualifications include experience with LLMs, Python, PyTorch/Jax, and interpretability methods.
Would you like to join a team curious about understanding how foundation models work and to expand their capabilities in scientific domains? We perform and publish novel research and apply our findings to drive product directions. If you like designing clever approaches to understanding complex phenomena and using that knowledge to solve practical problems then this is the position for you.
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.
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.
Minimum Qualifications
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.
Preferred Qualifications
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.
