Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Principal Machine Learning Engineer at Pinterest
Jinfeng is a principal engineer (director) at Pinterest, driving company’s technical directions on LLM, RecSys, Search. Previously, Jinfeng was an Engineering Manager at Google Gemini (now Google DeepMind), where he led the Gemini post-training team for international languages. His team worked on the cutting-edge techniques for LLM post-training, such as RLHF, instruction tuning, multi-lingual co-training, etc. Before that, Jinfeng has worked at Facebook AI and Comcast AI Lab. At Facebook, he helped build their ML-based natural language generation (NLG) system from 0 to 1. At Comcast, he helped build their AI-based XFINITY voice search system that allows users to find shows/movies through voice requests on TV, which helped win the 69th Emmy Award (2017) for the technical contributions in advancing television technologies. Jinfeng got his PhD degree at the intersection of information retrieval, deep learning and NLP. During the PhD, Jinfeng published 30+ papers (16 first-authored) in the top NLP/ML conferences, like ACL, ICLR, KDD, etc.
University of Maryland
Doctor of Philosophy, Computer Science
N/A – Present
Zhejiang University
Bachelor's Degree, Computer Science
N/A – Present
Principal Machine Learning Engineer
December 1, 2025 – Present
Sr. Staff Machine Learning Engineer
September 1, 2023 – December 1, 2025
Engineering Manager, Staff Software Engineer, Google DeepMind/Gemini
June 1, 2020 – August 1, 2023
Mountain View, CA
Research Scientist
September 1, 2018 – May 1, 2020
Menlo Park, California
Comcast
Visiting Researcher, Deep Learning for Voice Search
September 1, 2016 – May 1, 2018
Washington D.C. Metro Area
Cultural Fit Analysis
The candidate's background is heavily focused on Machine Learning, NLP, and AI/LLM, which is a strong technical fit for a data-intensive role. However, the target role is 'Big Data Engineer', which typically emphasizes distributed systems, data warehousing, ETL, and data pipeline orchestration. While ML engineers often work with big data, the core responsibilities and skill sets can differ significantly. The resume does not explicitly highlight traditional big data engineering skills (e.g., Spark, Hadoop, Kafka, data modeling for warehouses, data governance). This creates a potential gap in direct cultural and operational fit for a pure Big Data Engineer role, despite strong overall technical prowess.
Soft Skills & Operational Fit
The candidate's resume indicates strong leadership and initiative, having led teams and initiated projects. Experience at multiple large tech companies suggests adaptability and ability to operate in complex environments. However, specific soft skill assessments are not available.