NLP Engineer в IT — рынок СНГ и Европы

Медиана NLP Engineer — $0/мес по данным Zorky CRM (0 активных вакансий с явной NLP-спецификой — пул узкий за счёт того что многие NLP-roles classified как general ML Engineer / Backend). NLP Engineer — стабильный premium-сегмент за счёт rare-skill combination (Python + linguistics intuition + classical NLP + modern transformers + speech если track включает ASR / TTS). Senior с production search / RAG / translation experience — $6500-9500. Senior в speech-companies (ElevenLabs / AssemblyAI / Deepgram / Speechmatics / Soundhound — Voice AI track) — $8000-13000. International remote в frontier NLP-companies (Cohere / DeepL / Grammarly / Lilt / Hugging Face) — $9000-15000+ Senior. Big Tech NLP (Google Search / Meta Translation / Microsoft Translator / Apple Siri / Amazon Alexa) — $13000-22000+ Senior. Премиум-доплаты: speech specialization (ASR + TTS deep) +15-25%, multilingual / cross-lingual expertise (especially low-resource languages) +10-20%, classical NLP + linguistics PhD background +10-15%.

Цифры основаны на small sample — для broader benchmarks смотрите ML Engineer и AI / LLM Engineer страницы. Junior — typical entry: Computational linguistics MS / Backend Middle + NLP portfolio (Hugging Face fine-tuning experience). Скачок Junior → Middle — после первого production NLP-feature (semantic search / sentiment classifier / NER / translation). Middle → Senior — multi-task NLP pipeline ownership + speech ASR / TTS expertise или multilingual mastery. Senior → Staff / Principal — org-wide NLP architecture + research-paper publication track. Career-flow: Computational linguist / Backend Middle / DS Middle (2-3 года) + NLP interest → NLP Engineer Junior (1-2 года) → Middle (2-3 года) → Senior → либо Speech Engineer specialization, либо AI Engineer pivot (LLM-product focus), либо Research (academic-track NLP).

Москва Senior NLP Engineer — $6000-9500/мес (Яндекс — крупнейший NLP-работодатель РФ для Translate + Search + Алиса + Yandex.GPT; Сбер.AI — GigaChat NLP team + Salute Speech ASR / TTS; ABBYY — document understanding leader, classical NLP + modern transformers hybrid; Тинькофф — chatbot + voice; Just AI — chatbot platform; ЦРТ — Центр речевых технологий, speech leader РФ; VK / Mail.ru — Поиск + AI; Грамота.ру). СПб $5500-8500 (JetBrains AI Assistant NLP team). Минск/Киев $5000-8000 Senior. Польша €6500-10500 gross Senior. Германия €70-110K/год Senior. 0% — удалёнка. Аутсорсеры (EPAM AI / Luxoft AI / Andersen / DataArt NLP Practice) — почти всегда remote, $7000-10500 Senior на US NLP-projects. Международные voice-AI / NLP-companies (ElevenLabs / AssemblyAI / Deepgram / Speechmatics / Soundhound / DeepL / Grammarly / Lilt / Cohere / Hugging Face) — full-remote $9000-15000+ Senior. Big Tech NLP (Google Search / Meta Translation / Microsoft Translator / Apple Siri / Amazon Alexa) — $13000-22000+ Senior + RSU.

Топ-5: Python, Hugging Face Transformers, spaCy, PyTorch, sentence-transformers. Python монопольно. Hugging Face Transformers mastery — single most important library 2026 (модели + datasets + tokenizers + PEFT + Accelerate). spaCy (production-grade NLP pipelines — NER + POS + dependency parsing + lemmatization — Cython быстрый, industry standard для traditional NLP). NLTK (academic / legacy). Gensim (Word2Vec / LDA legacy). BERTopic (modern topic modeling — embeddings-based, rising 2024+). Modern transformers: BERT family (DeBERTa-v3 — best base для classification + NER 2026, RoBERTa, ELECTRA), T5 family (text-to-text), BART, XLM-R + mT5 + NLLB (multilingual). Sentence embeddings: sentence-transformers + BGE + E5 + Stella + jina-embeddings-v3 (top на MTEB benchmark). LLMs для NLP: Llama 3.x / Mistral / Qwen 2.5 / Phi 3 для few-shot classification / extraction / generation. OpenAI / Anthropic / Cohere APIs для production tasks. Search & ranking: Elasticsearch + OpenSearch (BM25 + dense_vector hybrid — industry standard), Vespa (Yahoo — best для production search, complex ranking), Tantivy + Meilisearch (Rust alternatives), cross-encoder rerankers (BGE Reranker / Cohere Rerank). ASR (Speech-to-Text): Whisper-large-v3 + faster-whisper (CTranslate2-optimized — production), Wav2Vec 2.0, NVIDIA NeMo, commercial APIs (AssemblyAI / Deepgram / Speechmatics / OpenAI Whisper API). Russian: Yandex SpeechKit / Sber Salute Speech / ЦРТ VoiceKit / VOSK (open-source offline). TTS: ElevenLabs (доминирует 2026 — best quality), OpenAI TTS, Coqui TTS (XTTS-v2 voice cloning), Tortoise, Bark, StyleTTS 2. Russian: Yandex SpeechKit + Sber SaluteSpeech TTS. Russian NLP-specific: ruBERT + RuRoBERTa (DeepPavlov — крупнейший русскоязычный NLP-проект), ruGPT, FRED-T5 (Sber), ruT5, USER-bge-m3 (Russian embeddings — топ на ruMTEB), Natasha (Russian NER + извлечение), Razdel (Russian tokenization), pymorphy3 (морфологический анализ). Audio processing: librosa + soundfile + torchaudio. Datasets: Hugging Face Hub (>200K NLP datasets — must use), Common Crawl + OSCAR (corpus), FLORES-200 (translation benchmark), MTEB + ruMTEB (embeddings benchmarks). Evaluation: classical NLP metrics (BLEU + ROUGE + METEOR + chrF для translation, F1 + precision + recall для NER, perplexity для LM, WER для ASR, MOS для TTS), modern eval — RAGAS + DeepEval для LLM-based NLP, COMET (neural translation eval). Annotation tools: Label Studio (open-source — industry standard 2026), Prodigy (Explosion / spaCy создатели), Doccano, Argilla (modern LLM-aware). Linguistic resources: WordNet, Universal Dependencies, BabelNet (multilingual).

Эти три роли сильно overlap в 2026 за счёт unification под трансформер-архитектуру, но различия есть. ML Engineer — generalist, владеет всем production ML stack (recsys / fraud / ranking / classical ML + LLM). Стек: PyTorch + sklearn + Kubernetes + MLflow + cloud-managed ML. См. ML Engineer. AI Engineer / LLM Engineer — focus на LLM integration в продукт (chatbots / RAG / agents). Стек: LangChain / LlamaIndex + Vector DBs + OpenAI / Anthropic APIs + vLLM serving + LoRA fine-tuning. См. AI / LLM Engineer. NLP Engineer (эта страница) — focus на natural language processing tasks specifically: NER / sentiment / search / translation / summarization / Q&A / speech (ASR + TTS). Stack overlap с AI Engineer ~60% — оба используют Hugging Face, embeddings, LLMs. Distinctions: 1) Classical NLP knowledge — NLP Engineer владеет pre-transformer techniques (BM25 + TF-IDF + Word2Vec + LDA + dependency parsing + NER pre-BERT), AI Engineer часто этого не знает (LLM-only). 2) Speech expertise — ASR (Whisper / Wav2Vec) + TTS (ElevenLabs / Tacotron) — exclusive NLP Engineer territory (AI Engineer редко touches speech). 3) Linguistics intuition — NLP Engineer часто имеет computational linguistics background (morphology / syntax / semantics formal training), AI Engineer обычно generalist Backend / ML. 4) Multilingual / low-resource languages — NLP Engineer specialty (cross-lingual transfer, NLLB, mT5). 5) Search & ranking deep — Elasticsearch + Vespa + production ranking pipelines — NLP Engineer territory. Career-pivots: NLP Engineer Senior → AI Engineer — easy lateral (1-3 месяца — добавить LangChain + agent frameworks). AI Engineer Senior → NLP Engineer — 3-6 месяцев (классические NLP techniques + speech знание занимает время). ML Engineer Senior → NLP Engineer — 4-8 месяцев. Reality 2026: title NLP Engineer уступает место AI Engineer в job postings (LLM hype), но классические NLP-tasks (search / translation / extraction / speech) остаются core production needs.

Decision tree для NLP techniques 2026: 1) Classical NLP only (без LLM) — best для: a) high-volume / low-latency production tasks (миллионы запросов в секунду — LLM API слишком дорого + медленно), b) on-device / edge / offline constraints (mobile keyboards / IoT devices), c) deterministic / explainable требования (legal / medical — нужно показывать «как пришли к ответу»), d) low-resource languages где LLM не покрывают (regional dialects / minority languages). Stack: spaCy + scikit-learn + Gensim + FastText. Examples: real-time spam filter, search query parser, keyboard predictive text, mobile sentiment widget. 2) Small transformer fine-tuned (без LLM) — best для: production NLP tasks где latency / cost важен но нужна высокая точность. Stack: DeBERTa-v3 / RoBERTa / XLM-R fine-tuned + ONNX export + TorchServe / Triton. Examples: production NER (extract entities из millions documents), text classification (sentiment / topic / intent), search ranking (cross-encoder reranker). Cost: $0.001-0.01 per request vs $0.01-1.00 для LLM API. Latency: 10-100ms vs 500-5000ms для LLM. 3) LLM zero-shot / few-shot (no training) — best для: a) prototyping (валидируем идею за день вместо месяца), b) long-tail tasks (rare classes где fine-tuning не оправдан), c) tasks requiring world knowledge / reasoning (multi-step inference, complex extraction). Stack: OpenAI / Anthropic / Cohere APIs + LangChain. Examples: complex document understanding, multi-step Q&A, creative writing assistance. 4) LLM fine-tuned (LoRA / QLoRA) — best когда zero-shot не хватает + classical / small transformer недостаточно гибки. Stack: Llama 3.x / Mistral / Qwen + PEFT + Unsloth. Examples: domain-specific chatbot (medical / legal style + knowledge), specialized code generation. 5) Hybrid (classical + LLM) — production reality 2026. Examples: a) Search — BM25 retrieval (classical) → dense retrieval (sentence-transformers) → LLM reranker (slow but accurate). b) RAG — chunking + spaCy preprocessing (classical) → embeddings (sentence-transformers) → vector search → LLM generation. c) NER → classical for high-confidence common entities (people / orgs / dates), LLM для long-tail extraction (custom domain entities). d) Translation — neural MT (mBART / NLLB / Marian) for common pairs, LLM for low-resource или style-specific. Cost reality 2026: production system с 100M tokens/day. Classical-only: $0-50/month compute. Small transformer-only: $500-5000/month (GPU). LLM API only: $50000-300000/month. Hybrid: $1000-15000/month (LLM only для hard cases, ~5-10% requests). Default choice 2026: start с LLM-prototype (validates value), then optimize — переносить high-volume tasks к small fine-tuned transformers, keep LLM для long-tail.

Да, 0% NLP Engineer-вакансий — full-remote или гибрид. NLP work fully cloud-based (training в cloud GPUs, models в Hugging Face Hub, datasets streaming, deployment в Kubernetes). Аутсорсеры (EPAM AI / Luxoft AI / Andersen / DataArt NLP Practice) — почти всегда remote на US NLP-projects. Российские (Яндекс Translate / Алиса / Search / Сбер.AI Salute Speech / Тинькофф chatbot / Just AI / ABBYY / ЦРТ) — гибрид или remote после probation. Российские банки — гибрид/офис. Международные voice-AI companies (ElevenLabs / AssemblyAI / Deepgram / Speechmatics / Soundhound) — full-remote standard. NLP-companies (DeepL — German / Grammarly / Lilt / Cohere / Hugging Face) — full-remote-friendly. Big Tech NLP (Google Search / Meta Translation / Microsoft Translator / Apple Siri / Amazon Alexa) — гибрид-standard. Релокант-хабы для NLP: США (Bay Area + NYC — major NLP labs density), UK (London — DeepMind NLP team), Канада (Toronto — Mila / Vector Institute), Германия (Berlin — DeepL HQ + Aleph Alpha), Франция (Paris — Hugging Face HQ + Mistral), Сингапур, Израиль (Tel Aviv — AI21 Labs). Английский для international NLP-remote — must (вся NLP literature + community + conferences ACL / EMNLP / NAACL англоязычные).

Speech Engineer — sub-specialization внутри NLP focused на voice domain. Day-to-day: ASR (Speech Recognition): deploy Whisper / Wav2Vec / NeMo for transcription pipelines, fine-tune for domain-specific terminology (medical / legal / customer support call centers — accuracy mandate), real-time streaming ASR (WebRTC + chunked processing + endpoint detection), speaker diarization (who said what — pyannote / NVIDIA NeMo Speaker), noise robustness (denoising + voice activity detection), multilingual + code-switching support. TTS (Speech Synthesis): deploy ElevenLabs / Coqui XTTS / Tacotron / VITS for voice generation, fine-tune for brand-specific voices, voice cloning (XTTS-v2 — clone из 6 seconds reference audio), prosody control (intonation + pacing + emotion), multilingual TTS, low-latency streaming TTS для real-time agents. Voice agents (rising 2024+): conversational AI с voice — combine ASR + LLM + TTS в low-latency pipeline (target <500ms response). Vapi / Retell AI / Pipecat — emerging open-source frameworks. Audio processing fundamentals: librosa + soundfile + torchaudio mastery, MFCC features, spectrograms, sample rate handling, codec знание (Opus / AAC / WAV). Stack-specific: NVIDIA NeMo (ASR + TTS unified framework), ESPnet (academic / research), SpeechBrain (PyTorch-based). Commercial APIs: AssemblyAI / Deepgram / Speechmatics / OpenAI Whisper API + Realtime API (Oct 2024 — voice agents) / ElevenLabs / Soundhound. Russian-specific: Yandex SpeechKit (премиум для русского — STT + TTS), Sber SaluteSpeech (banking voice + GigaChat voice), ЦРТ VoiceKit, VOSK (open-source offline). Зарплаты: Senior Speech Engineer — премиум над general NLP +15-25% за счёт rare-skill (audio processing skills + ML hybrid редки). $7000-12000 Senior в РФ tech, $8000-13000 в speech-companies (AssemblyAI / ElevenLabs / Deepgram). Topе $15000-25000+ Senior в Big Tech voice (Apple Siri / Amazon Alexa / Google Assistant). Career-flow: NLP Engineer Senior + audio interest + Whisper/Wav2Vec hands-on portfolio → Speech Engineer Junior / Middle — 4-8 месяцев.

В топе: Яндекс, Сбер.AI, ABBYY. Российские NLP-крупняки: Яндекс — крупнейший NLP-работодатель РФ (Translate — крупнейший русский NMT-проект; Search — поисковые NLP-pipelines; Алиса — voice + dialogue; Yandex.GPT — LLM; Маркет — search semantic + product NER). Сбер.AI (GigaChat NLP team + Salute Speech ASR / TTS + SberDevices voice assistants + banking text classification). ABBYY (legacy gigant — document understanding + OCR + NER + relation extraction — classical NLP + modern transformers hybrid; FineReader engine). Тинькофф (chatbot + voice assistant + transaction categorization + sentiment monitoring). Just AI (chatbot platform — JAICP — крупнейший в РФ для enterprise). ЦРТ — Центр речевых технологий (speech leader РФ — call center analytics + biometric voice). VK / Mail.ru (Mail.ru Поиск + Search + AI assistants для VK Cloud / Календарь / Disk). iSpring (educational NLP). Грамота.ру (computational linguistics для русского). Аутсорсеры: EPAM AI / NLP Practice (крупнейший AI-аутсорс в СНГ для US-NLP projects), Luxoft AI, Andersen AI, DataArt NLP, Itransition. International voice-AI companies (full-remote премиум): ElevenLabs (TTS leader 2026), AssemblyAI (ASR leader), Deepgram (real-time ASR + voice agents), Speechmatics (UK enterprise ASR), Soundhound (voice + music recognition), Vapi + Retell AI + Pipecat (rising voice agent platforms). NLP-companies: Cohere (enterprise LLM с RAG focus — Canada/UK), Hugging Face (NLP-first identity — France / NYC), DeepL (translation leader — Germany / Cologne), Grammarly (text correction — US / Ukraine team historically big), Lilt (enterprise translation). Big Tech NLP (топ-tier salary): Google Search (largest NLP team in world — search + Bard NLP), Meta AI Translation (NLLB project), Microsoft Translator, Apple Siri, Amazon Alexa, Apple Intelligence NLP team. Y Combinator NLP-startups — премиум remote. Academic / research labs: Stanford NLP Group / CMU LTI / Edinburgh NLP / Allen Institute AI2 (LangChain / LlamaIndex / DSPy creators ecosystem).

Roadmap: 1) Linguistics fundamentals — basic understanding morphology + syntax + semantics + pragmatics. Helps build intuition. Книга: «Speech and Language Processing» Jurafsky / Martin (free online 3rd edition — bible of NLP, не нужно читать целиком, важные главы). 2) Python deep + ML basics — pandas + scikit-learn + PyTorch (basics). 3) Classical NLP — spaCy mastery + NLTK exposure. Build simple pipelines (NER + sentiment classifier + topic model с BERTopic). Курс: spaCy course (free, by Explosion — spaCy creators). 4) Modern transformers fundamentals — understand BERT / DeBERTa / T5 architectures, fine-tuning workflow. Hugging Face NLP course (free, must-do — best resource 2026). 5) Hands-on Hugging Face Transformers — fine-tune DeBERTa-v3 на own classification dataset, fine-tune T5 на own summarization task. 6) Sentence embeddings — sentence-transformers + BGE + E5. Build semantic search demo. Understand MTEB benchmark. 7) LLMs для NLP tasks — OpenAI / Anthropic APIs для few-shot classification / extraction / generation. Compare prompt-only vs fine-tuned approaches на same task. 8) Search & ranking — Elasticsearch / OpenSearch deep с hybrid (BM25 + dense). Set up production search demo. Cross-encoder reranking (BGE Reranker). 9) Speech track (optional but премиум) — install Whisper / faster-whisper, build transcription pipeline + speaker diarization (pyannote). Try ElevenLabs TTS + Coqui XTTS-v2 voice cloning. 10) Russian NLP specific (для РФ-projects) — DeepPavlov framework (ruBERT + RuRoBERTa + ruGPT pre-trained models), Natasha (Russian NER), pymorphy3 (morphology), USER-bge-m3 (Russian embeddings). 11) Annotation tooling — Label Studio (industry standard 2026) — set up project + annotate small dataset + train custom model. 12) Evaluation methodology — classical metrics (BLEU / ROUGE / F1 / WER / MOS) + modern (RAGAS / DeepEval). 13) Multilingual exposure (если cross-lingual interest) — XLM-R + mT5 + NLLB-200 (Meta — 200 languages translation). 14) Pet-проект portfolio: a) production NER pipeline с custom domain (e.g. job descriptions extraction); b) semantic search для open dataset; c) Russian text classification fine-tuned ruBERT; d) voice agent demo (ASR + LLM + TTS в одной pipeline). Document на GitHub + blog post. Курсы РФ: Karpov.Courses «NLP» track, Otus «NLP», МФТИ DLSchool (NLP модуль), SkillFactory NLP, School21 (Сбер) NLP track, DeepPavlov community courses. International (eng): Hugging Face NLP Course (free, must-do), Stanford CS224N «NLP with Deep Learning» (free YouTube — best academic course), «Speech and Language Processing» Jurafsky / Martin (free PDF — bible), «Practical Natural Language Processing» Vajjala / Majumder / Gupta / Surana (O'Reilly, applied focus), fast.ai Practical Deep Learning Part 2 (NLP coverage). Books-must: «Natural Language Processing with Transformers» Tunstall / Werra / Wolf (Hugging Face authors — must-read 2026), «Speech and Language Processing» Jurafsky / Martin. Communities: r/LanguageTechnology, Hugging Face Discord (largest), DeepPavlov community (Russian), Telegram @nlp_ru, @ai_engineer_ru. ACL / EMNLP / NAACL conferences (papers must-follow для serious NLP track). Backend Senior / DS Middle + NLP interest → NLP Engineer Junior — 4-10 месяцев.

0 активных открытых NLP Engineer-вакансий с явной NLP-спецификой в нашей выборке. Реальный пул многократно шире — many NLP roles classified как general ML Engineer / Backend / AI Engineer (titles типа «ML Engineer для chatbot» или «Backend Engineer with NLP focus»). True NLP-focused dev jobs в СНГ + Европе оценочно 200-800 позиций активных любой момент 2026 (с учётом нечётко классифицированных). География: 🇷🇺 Россия / 🇵🇱 Польша / remote. Источники: hh.ru (особенно Яндекс / Сбер.AI / ABBYY active), Habr Career, getmatch, Djinni, LinkedIn (огромный международный NLP-сегмент через voice-AI companies + Big Tech), NoFluffJobs / JustJoin.it (Польша NLP-friendly), Telegram (@nlp_ru, @ml_jobs, @aijobs, @jobsforaiml, @ds_chat), карьерные сайты EPAM AI Practice / Luxoft AI / Andersen / DataArt, специализированные борды (aijobs.net, ai-jobs.net, builtin.com/jobs/ai), voice-AI / NLP direct careers (ElevenLabs / AssemblyAI / Deepgram / Speechmatics / Cohere / Hugging Face / DeepL / Grammarly / Lilt), ACL / EMNLP / NAACL conference job boards, Y Combinator Work at a Startup. Реальный рынок шире за счёт международного remote-сегмента (voice-AI + NLP-companies — full-remote-friendly). Время закрытия Senior NLP Engineer — 6-12 недель (longer чем general AI Engineer за счёт rare-skill combination — linguistics + ML + audio if speech track).

Senior NLP Engineer владеет полным циклом NLP-product engineering + technical leadership. Python deep + Backend Senior level: async / typing / FastAPI / pytest mastery. Linguistics intuition: morphology + syntax + semantics + pragmatics — на уровне «понимаю почему модель ошибается в этом сложном случае». Не нужен formal linguistics degree, но знание базы critical. Hugging Face Transformers mastery: модели (BERT family + T5 + LLM) + datasets + tokenizers + PEFT + Accelerate. Fine-tuning mastery (LoRA / QLoRA + full fine-tuning когда оправдано). spaCy mastery: production NLP pipelines (NER + POS + dependency parsing + custom components + matchers), spaCy-transformers integration. Modern transformers: DeBERTa-v3 (best base для classification + NER 2026), T5 family, XLM-R / mT5 / NLLB (multilingual). Understand attention + tokenization + decoding strategies. Sentence embeddings mastery: sentence-transformers + BGE + E5 + Stella + jina-embeddings-v3, training own custom embeddings (contrastive loss + multi-negative ranking). Search & ranking mastery: Elasticsearch + OpenSearch advanced (BM25 + dense_vector hybrid + custom analyzers + multi-language support), Vespa для complex ranking pipelines, cross-encoder rerankers (training own BGE Reranker variants). LLM integration для NLP tasks: prompt engineering для NER / extraction / classification, few-shot vs fine-tuned trade-off analysis, structured output (function calling). Speech mastery (if track включает): Whisper / Wav2Vec deep (fine-tuning for domain), pyannote speaker diarization, audio processing fundamentals (librosa + torchaudio), real-time streaming ASR architecture, voice agents architecture (ASR + LLM + TTS low-latency pipeline). Multilingual / low-resource expertise: cross-lingual transfer learning (XLM-R / mT5), data augmentation для low-resource languages, multilingual evaluation methodology. Russian NLP specifically (если РФ-focused): DeepPavlov mastery, Natasha advanced, custom russian-specific tokenization / morphology handling. Classical NLP knowledge: TF-IDF + BM25 internals, Word2Vec / GloVe / FastText, dependency parsing algorithms, CRF for sequence labeling — для понимания когда classical beat LLM (cost / latency / explainability). Annotation tooling mastery: Label Studio advanced + Argilla (modern LLM-aware) + Prodigy (spaCy ecosystem). Evaluation mastery: classical metrics (BLEU + ROUGE + METEOR + chrF + F1 + perplexity + WER + MOS), modern (RAGAS + DeepEval + COMET для translation), human eval methodology design. System design для NLP products: design NLP pipeline на whiteboard под scale (100M+ texts/day), latency budgets (target P95 для real-time NLP), cost optimization (cache + batch + smart routing). Soft: ADRs writing для NLP architecture decisions, technical writing (NLP feature design docs + evaluation reports), cross-team collaboration (Product / Backend / DS / Linguists teams), mentoring Middle NLP engineers, paper-reading discipline (ACL / EMNLP / NAACL / Interspeech если speech). Английский для Senior+ MUST — NLP community / docs / papers / conferences ACL / EMNLP / NAACL англоязычные. Optional bonus: open-source contributions в Hugging Face / spaCy / DeepPavlov / sentence-transformers — резко повышают market value. Papers at ACL workshops — премиум для frontier-NLP companies (Cohere / Hugging Face / DeepL) hiring.

Данные собраны автоматически из 1000+ источников — Telegram-каналов вакансий и сайтов работы СНГ и Восточной Европы (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it, Pracuj.pl и других). Парсинг работает круглосуточно, дубликаты фильтруются по описанию и URL, аномальные значения зарплат отсекаются. Подробная методология — на странице «Как работает».