Ilnar Salimzianov's Personal Site

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Résumé

Ilnar Salimzianov — NLP / ML Engineer

Computational linguist by training — a decade building NLP and speech-to-text systems, especially for under-resourced languages, and taking them from data pipeline to production and on-device deployment.

Open to full-time roles — employment or long-term contract — remote, or on-site/hybrid in the EU or the Balkans (open to relocation). Hire me →

Websites

ifs.name, taruen.com

Contact

Email: ilnar@selimcan.org, Telegram, LinkedIn, GitLab, GitHub

Please also see the Projects page.

Programming skills

Languages: Python, Racket, Clojure, GNU Bash
Experience with: Data & Backend Infra: Elasticsearch, FastAPI, Pandas, Scrapy, Docker | MLOps & NLP: Azure ML, ONNX Runtime, HF Transformers, Scikit-learn, Spacy, NLTK | Low-Resource NLP: Apertium, HFST, VISL CG-3 | Testing: Pytest, pytest-bdd, Locust

Education

University of Stuttgart (2014-2017): M.Sc. degree in Computational Linguistics
Kazan State University (2006-2011): Specialist's degree in German Philology, focus Linguistics

Natural Languages

Tatar (native), Russian, German (C1, TestDaF 5,5,5,5), English (C1, TOEFL iBT 112), Kazakh, Turkish

Experience

07/2025-08/2026 Regional Language Researcher (Independent Contractor)

Mozilla Data Collective (Remotely)

Sourced and helped external dataset owners publish ML-ready corpora on the Mozilla Data Collective — including Armenian speech corpora (question–answer dialogues; refugee testimonials), a Kyrgyz human-annotated NER dataset, a Kyrgyz LLM-evaluation benchmark, and a Georgian logical-reasoning LLM benchmark.
Onboarded 8 archival speech corpora from the INEL project at the University of Hamburg (Institute for Finno-Ugric/Uralic Studies) and 25 news-text corpora from a major international broadcaster.
Created, scraped, and published 12 ML-ready datasets under the Taruen account: folklore and literature corpora (Tatar, Kyrgyz, Dolgan, Finnish, Polish), proverb collections (Kazakh, Uzbek, Kumyk, Chechen, Bulgarian), a Chuvash TTS speech set, and a structured World Factbook JSON dataset.
Wrote datasheets and standardized packaging so each corpus is directly ingestible for downstream LLM/NLP training.

01/2024-12/2025 Computational Linguist / NLP Developer (Independent Contractor)

US LegalTech Startup (Remotely)

Architected and deployed an end-to-end data ingestion pipeline, scraping and processing over 20 million public trademark records from the USPTO API.
Designed and optimized a highly complex Elasticsearch schema capable of executing multi-layered similarity queries, including phonetic matching, orthographic homoglyphs, and cross-lingual semantic translations.
Built a RESTful API utilizing FastAPI to serve real-time trademark clearance search results to downstream frontend applications.
Drove development test-first: wrote business-readable acceptance tests with pytest-bdd (Given/When/Then scenarios with example tables), so non-technical stakeholders could review and sign off on behavior.
Implemented automated data clearance pipelines to isolate and verify so-called distinctive components within registered trademarks.

08/2021-10/2023 Computational linguist / NLP developer

Orpheus Technology Ltd (prowritingaid.com)

Helped launch ProWritingAid's premium-tier tone detection feature by training and fine-tuning models using Scikit-learn and Huggingface Transformers.
Deployed models on Microsoft Azure ML and monitored performance over time to ensure zero downtime.
Converted PyTorch models into ONNX format, optimizing and quantizing them for specific Azure ML hardware using the Huggingface Optimum library.
Load-tested Azure ML endpoints using Locust to right-size instance types; the ONNX-quantized models let a workload the team had originally scoped for 8 GPU machines run on 1 (with a second added for redundancy), at negligible accuracy loss.
Used Large Language Models (ChatGPT, Claude) for data generation and labeling, and fine-tuned models via the OpenAI API.
Prototyped GUI apps in Racket (gui-easy) and Clojure (cljfx) to explore combining ProWritingAid with LLMs.

01/2018-11/2021 Remote research assistant/computational linguist

Nazarbayev University, Nur-Sultan, Kazakhstan

Trained a new speech-to-text system for Kazakh using the Coqui STT framework (details: https://arxiv.org/abs/2107.10637).
Wrote a web interface to an existing ESPnet-based Kazakh speech-to-text system and packaged the app into a Docker image.
Gathered data and led the launching of https://commonvoice.mozilla.org/kk and https://commonvoice.mozilla.org/tt.
Extended the Kazakh morphological transducer apertium-kaz with new stems, affixes, and a Constraint Grammar including dependency parsing capabilities in the Universal Dependencies framework.

07/2017-03/2018 Remote software contractor

Central Eurasian Studies Department, Indiana University, Bloomington (United States)

Developed a closed domain, rule-based Tatar-to-English machine translator to translate Tatar population records (1828-1918).

04/2015-03/2017 Research assistant

Institute for Natural Language Processing, University of Stuttgart

Developed a dependency-based sentence simplifier/compressor for German.

05/2014-08/2014 Student developer

Apertium project as part of the Google Summer of Code 2014 programme

Developed a prototype Tatar-Russian machine translator.

05/2012-08/2012 Student developer

Apertium project, Google Summer of Code 2012 programme

Developed a rule-based Kazakh-Tatar machine translator.

Independent Coursework

MITx 6.00.1x: Introduction to Computer Science and Programming Using Python = 93% (on edx.org)
MITx 6.00.2x Introduction to Computational Thinking and Data Science = 88% (on edx.org)
UTAustinX UT.5.02x Linear Algebra - Foundations to Frontiers = 62% (on edx.org)
SPD1x: Systematic Program Design - Part 1
Andrew Ng's "Machine Learning" course

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