Hi, I’m Evgeny! Currently, I work at Mozilla as a Senior Software Engineer in Machine Learning. My main area of interest is prototyping and deploying AI products in a full-stack manner – from Jupyter notebooks, ETL and training pipelines to scalable production services. I have experience shipping Deep Learning and Classical ML projects, including NLP/NLU, Machine Translation, Recommender Systems and Computer Vision in various companies. I’m based in Vancouver, Canada.
Current AI projects
- Firefox Translations (Mozilla) – efficient on-device Neural Machine Translation in a browser (this work is a part of the EU-funded project Bergamot). I developed a scalable end-to-end training pipeline in collaboration with researchers from the University of Edinburgh, configured training infrastructure and trained translation models for several languages. Development of Firefox Translations web extension. (python, bash, C++, Slurm, Snakemake, JavaScript)
- Firefox Addon Recommender (Mozilla) – recommends Firefox addons on Firefox Settings tab and AMO (related add-ons). Migration to GCP, maintenance, ETL pipelines, analytics (Python, GCP, scikit-learn, Spark, Airflow)
- Pocket recommendations (Mozilla) – similar content and “For you” recommendations (Python, Gensim, Lightfm, Metaflow, AWS, Qdrant)
Past AI projects
- Press Reader Deep PDF (2020) – Deep Learning based segmentation and block classification for PDFs of newspapers and magazines (Pytorch, Kubeflow, GCP)
- Press Reader recommender (2019) – Deep Learning news recommender (Tensorflow, Python, k8s, GCP)
- Native AI recommender (2019) – Deep Learning based “Read more” recommender for third-party websites (Tensorflow, Python, k8s, GCP)
- News360 recommender (2018) – personalized news feed. Deep Learning based recommender, training pipelines, training infrastructure, the subsystem for AB experiments (Tensorflow, Python, Airflow, k8s)
- News360 NLP platform (2017) – designed and implemented microservices for extraction of topics and entities from news articles (batch processing, inference of NLP ML models, Python, XGBoost).
- Cognitive Forms (2008-2011) – OCR for structured documents. I developed an algorithm for multi-field contextual recognition as my diploma project. (C++)
Engineering
- News360 (2016) – news crawler, analytics, migration of backend services from Windows C# stack to Python Linux stack using microservices architecture
- Native AI analytics (2017) – monetization backend, analytics pipelines on Spark
- NPTV (2013-2015) – C# framework for the development of interactive TV apps
- Trud Expert (2011-2013) – desktop C# applicaiton on WPF
Pet projects
- llm_sum – styled summarization with Large Language models
- Attractions recommender – personalized recommendations of cool places to visit in British Columbia based on TripAdvisor reviews (Python, Pandas, Dask, Spacy, Gensim, ElasticSearch, React.js)
- redhairtravel.com – travel blog on WordPress
Education
Artificial Intelligence Program, Stanford, 2021-2023
Master’s Degree in Applied Mathematics, MISIS, 2006-2011
Courses
- Natural Language Understanding (Stanford Center for Professional Development, cs224u, 2023)
- Machine Learning with Graphs (Stanford Center for Professional Development, cs224w 2022)
- Natural Language Processing with Deep Learning (Stanford Center for Professional Development, cs224n, 2021)
- Deep Learning specialization (deeplearning.ai, Coursera, 2018)
- Machine Learning (Stanford, Coursera, 2017)
Publications and talks
- Summarizing in style: Exploring summarization with Large Language Models (Stanford cs224u, 2023)
- Training efficient neural network models for Firefox Translations (Mozilla Hacks, 2022)
- Microservices Workshop (Highload++, 2016)
Hobbies
- Music (electric guitar, drums)
- Travelling (30+ countries)
- Hiking, tennis, sailing and other random outdoor stuff