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AI Performance Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Looking for interesting, hard problems. Currently, focusing on ML related topics, such as image processing, NLP, signal processing ... Desired to understand the theory and apply it.
Technical University of Munich
Exchange student, Computer Science
January 1, 2018 – January 1, 2019
Budapest University of Technology and Economics
Master of Science - MS, Computer Engineering
January 1, 2017 – January 1, 2019
Budapest University of Technology and Economics
Bachelor of Science - BS, Computer Engineering with Intelligent Systems major
January 1, 2013 – January 1, 2017
Meta
Software Engineer in Machine Learning
September 1, 2023 – Present
Oslo, Norway · Hybrid
3LC.AI
Senior AI Engineer
January 1, 2023 – September 1, 2023
Oslo, Norway · Hybrid
Graphcore
Artificial Intelligence Engineer
June 1, 2020 – December 1, 2022
Oslo, Norway
Continental
Machine Learning Expert
June 1, 2019 – May 1, 2020
Budapest
The Curious AI Company
Machine Learning Intern
June 1, 2018 – September 1, 2018
Helsinki
LogMeIn
Machine Learning Engineer Intern
October 1, 2016 – June 1, 2018
Budapest
Everex Financial Solutions
Stock Analyst
February 1, 2016 – September 1, 2016
Budapest
Mono Project
Google Summer of Code
May 1, 2015 – August 1, 2015
EncoSoft
Software Developer
January 1, 2014 – August 1, 2014
Budapest
Cultural Fit Analysis
The candidate has a strong background in ML engineering across various companies, from startups (3LC.AI, The Curious AI Company) to large corporations (Meta, Continental, LogMeIn) and specialized hardware companies (Graphcore). This diversity indicates adaptability to different work environments and project types. Their roles consistently align with Machine Learning, demonstrating a clear career path and passion for the domain. The breadth of experience, including research-oriented roles and practical application, suggests a well-rounded individual who can contribute to various aspects of an ML team. However, the lack of explicit project details or community involvement makes it difficult to assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest a problem-solving mindset, particularly in optimizing and scaling complex ML systems. Their work on finding conflicting assumptions and testing different parallelisms indicates strong analytical and debugging skills. The participation in a hackathon and achieving an award also points to innovation and initiative. However, without direct assessment data, specific soft skills like teamwork, leadership, or communication style cannot be definitively evaluated.