Maxim Vakhrushev
Data AnalystSenior ML Engineer | Tech Lead
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior ML Engineer | Tech Lead
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior ML Engineer / Tech Lead with ~8 years of experience building and shipping production ML systems. Currently leading the end-to-end development of LMs for JetBrains IDEs features, both cloud and on-device. Previously delivered production Computer Vision systems processing satellite imagery and street-level videos, and built low-latency distributed ML infrastructure at scale.
Novosibirsk State University (NSU)
Master's degree, Computer Science
January 1, 2015 – January 1, 2017
Yandex School of Data Analysis
Master's degree, Data Analysis
January 1, 2015 – January 1, 2017
Novosibirsk State University (NSU)
Bachelor's degree, Computer Science
January 1, 2011 – January 1, 2015
JetBrains
Technical Lead
July 1, 2024 – Present
Munich, Bavaria, Germany
JetBrains
Machine Learning Engineer
October 1, 2023 – Present
Munich, Bavaria, Germany
Joom
Machine Learning Engineer
December 1, 2021 – July 1, 2023
Yerevan, Armenia
2GIS
Technical Lead
August 1, 2020 – September 1, 2021
Novosibirsk, Russia
2GIS
Machine Learning Engineer
April 1, 2018 – September 1, 2021
Novosibirsk, Russia
Computer Science Center
Teacher Assistant
February 1, 2018 – June 1, 2019
Novosibirsk, Russia
Yandex School of Data Analysis
Teacher Assistant
February 1, 2017 – January 1, 2018
Novosibirsk, Russia
Parallels-NSU Laboratory
Software Engineer Intern
June 1, 2013 – November 1, 2015
Novosibirsk, Russia
8th place, Top 1%, TensorFlow Speech Recognition Challenge
December 1, 2017 – January 1, 2018
In a team of five people built an algorithm that understands simple spoken commands. The final solution combines Deep Convolutional Neural Networks and classical audio processing approaches. The developed algorithm allowed my team to achieve 8th result out of more than 1300 participants. Top 1% (Gold medal, 8th out of 1315 teams).
Top 13%: Carvana Image Masking Challenge
August 1, 2017 – September 1, 2017
As a part of the Carvana Image Masking Challenge developed a high-performance pipeline that utilizes classical encoder-decoder U-Net networks (Keras, Tensorflow) and traditional computer vision methods (OpenCV). Top 13% (Bronze medal, 95th out of 700 teams).
Top 18%: Planet: Understanding the Amazon from Space
June 1, 2017 – July 1, 2017
As a part of the Planet: Understanding the Amazon from Space challenge developed a high-performance pipeline that utilizes Deep Convolutional Neural Networks to perform multiclass labeling of satellite images. Top 18% (164th out of 950)
Top 5%: Google Cloud & YouTube-8M Video Understanding Challenge
May 1, 2017 – June 1, 2017
As a part of Google Cloud & YouTube-8M Video Understanding Challenge developed an algorithm that combines deep neural networks (Keras, TensorFlow) to perform multilabel classification of YouTube videos. Top 5% (SIlver medal, 32nd out of 655 teams).
Top 8%: Quora Question Pairs
May 1, 2017 – June 1, 2017
As a part of Quora Question Pairs challenge developed an algorithm that solves natural language processing problem of classification whether question pairs are duplicates or not. The final pipeline uses Gradient Boosting model (xgboost) and traditional natural language processing methods (nltk). Top 8% (Bronze medal, 264th out of 3307 teams)
Top 4%: Two Sigma Connect: Rental Listing Inquiries
March 1, 2017 – April 1, 2017
As a part of Two Sigma Connect: Rental Listing Inquiries challenge developed an algorithm that predicts the number of inquiries a new rental listing receives based on the listing’s creation date and other features. Used ensemble of Gradient Boosting, Random Forest, Logistic Regression and Nearest Neighbors models. Top 4% (Silver medal, 94th out of 2488)
Cultural Fit Analysis
The candidate's extensive participation and high rankings in numerous data science competitions (Kaggle) demonstrate a strong passion for data analysis and machine learning, aligning well with a data-driven culture. Their experience as a Technical Lead and Machine Learning Engineer in various companies (JetBrains, Joom, 2GIS) shows adaptability to different team structures and project types. However, the target role is 'Data Analyst', while the candidate's experience is heavily skewed towards 'Machine Learning Engineer' and 'Technical Lead' with a focus on model development and MLOps. This might indicate a potential mismatch in the day-to-day responsibilities and expectations for a pure Data Analyst role, which typically involves more business intelligence, reporting, and descriptive analytics rather than advanced model building and deployment. While the underlying analytical skills are strong, the direct role alignment is not perfect.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong drive for achieving top performance and a collaborative spirit (e.g., 'In a team of five people built an algorithm'). Experience as a Technical Lead indicates leadership and project ownership. The teaching assistant roles suggest an ability to explain complex concepts and mentor. The focus on optimizing pipelines and reducing storage usage points to an operational mindset.