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Senior AI/ML Research Engineer @ Huawei Noah’s Ark Lab
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I’m a Senior Research Engineer in Machine Learning at Huawei Noah’s Ark Lab. In the last few years, my work has centered around model-based reinforcement learning, large language models (LLMs), and AutoML, with publications at top-tier conferences (ICLR, ICML, NeurIPS). Alongside research, I have a strong background in software development. I contributed to scikit-learn and developed a benchmark library for model-based RL that was adopted by my team and which supported multiple research papers.
Télécom Paris
PhD, Machine learning
January 1, 2014 – January 1, 2017
Ecole Normale Supérieure de Cachan
Master's degree, Machine Learning, Computer Vision
January 1, 2012 – January 1, 2013
CentraleSupélec
Diplôme d'ingénieur des Grandes Ecoles, Applied Mathematics (Machine learning)
January 1, 2009 – January 1, 2013
Lycée Saint Louis
Classes Préparatoires, Mathematics/Physics
January 1, 2006 – January 1, 2009
Huawei Technologies
Senior AI/ML Research Engineer
November 1, 2017 – Present
Paris Area, France
scikit-learn
Open Source Machine Learning Engineer
June 1, 2017 – October 1, 2017
Airbus Group
PhD Researcher in Machine Learning
May 1, 2014 – April 1, 2017
Paris Area, France
Airbus Group
Research Intern
May 1, 2013 – October 1, 2013
Région de Paris, France
GE Aviation
Internship
March 1, 2012 – August 1, 2012
Région de Cincinnati, États-Unis
EDF
Research Intern
July 1, 2011 – November 1, 2011
Région de Paris, France
Cultural Fit Analysis
The candidate's background in both academic research and industry roles (Huawei, Airbus, scikit-learn) demonstrates adaptability. Their involvement in open-source development and diverse application areas (medical NLP, wireless networks, maintenance, cybersecurity) suggests a broad interest and ability to work across different domains, which aligns with a dynamic, research-oriented culture. However, the lack of explicit project diversity outside of research and core ML applications might limit fit for roles requiring broader software engineering or product development experience.
Soft Skills & Operational Fit
The candidate's experience in supervising PhD students and resolving engineering bottlenecks suggests strong problem-solving and mentorship skills. Their contributions to open-source projects and collaborative research indicate a capacity for teamwork and independent initiative. The detailed descriptions of their work imply good communication of technical concepts.