Help & Supportcareers@gravityer.com
Code RL/Code Behavior @Anthropic, Past: Post-Training/RL of Gemini @DeepMind
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Specialties: Reinforcement Learning, Post-Training More about me: https://sanazbahargam.github.io/
Boston University
PhD, Computer Science
N/A – Present
Anthropic
Member of Technical Staff
January 1, 2025 – Present
Google DeepMind
Research Engineer, TL
January 1, 2023 – January 1, 2025
Machine Learning Engineer, TL
January 1, 2023 – January 1, 2023
Amazon Lab126
Applied Scientist at Alexa and AGI Foundation
January 1, 2021 – January 1, 2023
Machine Learning Engineer
January 1, 2017 – January 1, 2021
San Francisco Bay Area
Stevens Institute of Technology
Visiting Researcher (intern)
May 1, 2016 – September 1, 2016
Hoboken, New Jersey
128 Technology
Machine Learning Researcher (intern)
May 1, 2015 – September 1, 2015
Burlington, Massachusetts
Cultural Fit Analysis
The candidate has worked at several leading technology companies known for innovation and fast-paced environments (Anthropic, Google DeepMind, Amazon Lab126, Twitter). Their experience spans research and applied roles, indicating adaptability and a willingness to tackle diverse challenges. The PhD background and continuous engagement with cutting-edge AI/NLP topics suggest a strong cultural alignment with organizations that value continuous learning, research, and pushing technological boundaries. The breadth of experience from recommendation systems to generative AI demonstrates versatility.
Soft Skills & Operational Fit
The candidate's resume highlights significant contributions to large-scale projects and leadership roles (TL), suggesting strong collaboration, problem-solving, and project management skills. The focus on post-training and RL for LLMs indicates an ability to handle complex, iterative development cycles and adapt to evolving research fronts. The lack of specific project descriptions beyond high-level responsibilities makes it difficult to fully assess operational fit, but the caliber of companies and roles implies a high degree of operational effectiveness.