AI Research Scientist - Language - Meta AI (mrs)

Meta · Big Tech · Menlo Park, CA

AI Research Scientist focused on advancing large language model research for recommendation systems and generative AI applications within Meta's consumer products (Facebook, Instagram, Threads).

What you'd actually do

Collaborate and execute on research that pushes forward the state of the art in large language model research
Directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results
Contribute to publications and open-sourcing efforts
Help identify long-term ambitious research goals as well as intermediate milestones
Prioritize research that can be applied to Meta's product development

Skills

Required

Experience in developing interactive solutions for computer vision, natural language processing, or computer graphics
Direct experience in generative AI and LLM research
Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
Experience with deep learning frameworks (such as pytorch, tensorflow) and Python
Experience with manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
Experience working and communicating cross-functionally in a team environment
Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)

Nice to have

work authorization

What the JD emphasized

Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), Computer Vision (CVPR, ICCV, ECCV)

Other signals

LLM research
generative AI
recommendation systems

Read full job description

The Meta AI team is working on advances in content understanding, user understanding, retrieval, and ranking. As a key driver of Meta's app growth, we are focused on improving recommendation systems by leveraging the latest advancements in AI, including Large Language Models (LLMs) and generative AI. We're dedicated to advancing recommendation systems to improve user experiences across Facebook, Instagram, Threads, and more.

Responsibilities

Collaborate and execute on research that pushes forward the state of the art in large language model research Directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results Contribute to publications and open-sourcing efforts Help identify long-term ambitious research goals as well as intermediate milestones Prioritize research that can be applied to Meta's product development

Qualifications

Experience in developing interactive solutions for computer vision, natural language processing, or computer graphics Direct experience in generative AI and LLM research Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), Computer Vision (CVPR, ICCV, ECCV) Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward Experience with deep learning frameworks (such as pytorch, tensorflow) and Python Experience with manipulating and analyzing complex, large scale, high-dimensionality data from varying sources Experience working and communicating cross-functionally in a team environment Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)