Staff ML Researcher - Open Models & Scalable AI

Sierra Ventures; San Francisco (CA); On-site; USD 210,000 - 300,000; Full time

Benefits offered by this job

Top-tier compensation

Stock options

Health & wellness

Meals

Parental leave

Unlimited PTO

Visa sponsorship

Team events

Job summary

Reflection is a research lab focused on making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI.

As a Research Scientist, you will design and run scientific experiments to advance our understanding of scaling large language models and data efficiency, and implement state-of-the-art methods from the deep learning literature.

Qualifications

• MS or PhD in CS, ML, or related field.
• Experience building large-scale systems.
• Experience with ETL workflows and training data prep.
• Strong Python and PyTorch experience.
• Ability to balance research goals with engineering practicality.

Responsibilities

• Research and build algorithms and models across scaling laws, data processing, optimizers, and architecture.
• Design and run experiments to advance understanding of scaling LLMs and data efficiency.
• Implement state-of-the-art methods from the deep learning literature.
• Lead small research projects independently while collaborating on larger initiatives.
• Optimize the training infrastructure for efficient scaling.
• Contribute across the stack from low-level optimizations to high-level model design.

Skills

Software engineering

ETL workflows

Deep learning / ML

Python

PyTorch

Distributed training

Communication

Education

MS or PhD in CS/ML

Tools

PyTorch

Job description

Reflection is a research lab focused on making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI.

As a Research Scientist, you will design and run scientific experiments to advance our understanding of scaling large language models and data efficiency, and implement state-of-the-art methods from the deep learning literature.