NLP Engineer in IT — CIS and Europe market

The median NLP Engineer salary is $0/mo per Zorky CRM data (0 active jobs with explicit NLP specifics — the pool is narrow because many NLP roles are classified as general ML Engineer / Backend). NLP Engineer — a steady premium segment thanks to the rare-skill combination (Python + linguistics intuition + classical NLP + modern transformers + speech if track includes ASR / TTS). Senior with production search / RAG / translation experience — $6,500-9,500. Senior at speech-companies (ElevenLabs / AssemblyAI / Deepgram / Speechmatics / Soundhound — Voice AI track) — $8,000-13,000. International remote at frontier NLP-companies (Cohere / DeepL / Grammarly / Lilt / Hugging Face) — $9,000-15,000+ Senior. Big Tech NLP (Google Search / Meta Translation / Microsoft Translator / Apple Siri / Amazon Alexa) — $13,000-22,000+ Senior. Premium add-ons: speech specialisation (ASR + TTS deep) +15-25%, multilingual / cross-lingual expertise (especially low-resource languages) +10-20%, classical NLP + linguistics PhD background +10-15%.

Numbers based on a small sample — for broader benchmarks see ML Engineer and AI / LLM Engineer pages. Junior — typical entry: Computational linguistics MS / Backend Middle + NLP portfolio (Hugging Face fine-tuning experience). Junior → Middle jump — after the first production NLP feature (semantic search / sentiment classifier / NER / translation). Middle → Senior — multi-task NLP pipeline ownership + speech ASR / TTS expertise or multilingual mastery. Senior → Staff / Principal — org-wide NLP architecture + research-paper publication track. Career flow: Computational linguist / Backend Middle / DS Middle (2-3 years) + NLP interest → NLP Engineer Junior (1-2 years) → Middle (2-3 years) → Senior → either Speech Engineer specialisation, AI Engineer pivot (LLM-product focus), or Research (academic-track NLP).

Moscow Senior NLP Engineer — $6,000-9,500/mo (Yandex — largest NLP employer in Russia for Translate + Search + Alice + Yandex.GPT; Sber.AI — GigaChat NLP team + Salute Speech ASR / TTS; ABBYY — document understanding leader, classical NLP + modern transformers hybrid; Tinkoff — chatbot + voice; Just AI — chatbot platform; STC — Speech Technology Center, speech leader in Russia; VK / Mail.ru — Search + AI; Gramota.ru). St Petersburg $5,500-8,500 (JetBrains AI Assistant NLP team). Minsk/Kyiv $5,000-8,000 Senior. Poland €6,500-10,500 gross Senior. Germany €70-110K/yr Senior. 0% remote. Outsourcing shops (EPAM AI / Luxoft AI / Andersen / DataArt NLP Practice) — almost always remote, $7,000-10,500 Senior on US NLP projects. International voice-AI / NLP companies (ElevenLabs / AssemblyAI / Deepgram / Speechmatics / Soundhound / DeepL / Grammarly / Lilt / Cohere / Hugging Face) — full-remote $9,000-15,000+ Senior. Big Tech NLP (Google Search / Meta Translation / Microsoft Translator / Apple Siri / Amazon Alexa) — $13,000-22,000+ Senior + RSU.

Top 5: Python, Hugging Face Transformers, spaCy, PyTorch, sentence-transformers. Python monopolistic. Hugging Face Transformers mastery — single most important library 2026 (models + datasets + tokenizers + PEFT + Accelerate). spaCy (production-grade NLP pipelines — NER + POS + dependency parsing + lemmatisation — Cython-fast, industry standard for traditional NLP). NLTK (academic / legacy). Gensim (Word2Vec / LDA legacy). BERTopic (modern topic modelling — embeddings-based, rising 2024+). Modern transformers: BERT family (DeBERTa-v3 — best base for 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 on the MTEB benchmark). LLMs for NLP: Llama 3.x / Mistral / Qwen 2.5 / Phi 3 for few-shot classification / extraction / generation. OpenAI / Anthropic / Cohere APIs for production tasks. Search & ranking: Elasticsearch + OpenSearch (BM25 + dense_vector hybrid — industry standard), Vespa (Yahoo — best for 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-optimised — production), Wav2Vec 2.0, NVIDIA NeMo, commercial APIs (AssemblyAI / Deepgram / Speechmatics / OpenAI Whisper API). Russian: Yandex SpeechKit / Sber Salute Speech / STC VoiceKit / VOSK (open-source offline). TTS: ElevenLabs (dominates 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 — largest Russian-language NLP project), ruGPT, FRED-T5 (Sber), ruT5, USER-bge-m3 (Russian embeddings — top on ruMTEB), Natasha (Russian NER + extraction), Razdel (Russian tokenisation), pymorphy3 (morphological analysis). 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 for translation, F1 + precision + recall for NER, perplexity for LM, WER for ASR, MOS for TTS), modern eval — RAGAS + DeepEval for LLM-based NLP, COMET (neural translation eval). Annotation tools: Label Studio (open-source — industry standard 2026), Prodigy (Explosion / spaCy creators), Doccano, Argilla (modern LLM-aware). Linguistic resources: WordNet, Universal Dependencies, BabelNet (multilingual).

These three roles overlap heavily in 2026 due to unification under the transformer architecture, but there are differences. ML Engineer — generalist, owns the whole production ML stack (recsys / fraud / ranking / classical ML + LLM). Stack: PyTorch + sklearn + Kubernetes + MLflow + cloud-managed ML. See ML Engineer. AI Engineer / LLM Engineer — focus on LLM integration into a product (chatbots / RAG / agents). Stack: LangChain / LlamaIndex + Vector DBs + OpenAI / Anthropic APIs + vLLM serving + LoRA fine-tuning. See AI / LLM Engineer. NLP Engineer (this page) — focus on natural-language-processing tasks specifically: NER / sentiment / search / translation / summarisation / Q&A / speech (ASR + TTS). Stack overlap with AI Engineer ~60% — both use Hugging Face, embeddings, LLMs. Distinctions: 1) Classical NLP knowledge — NLP Engineer owns pre-transformer techniques (BM25 + TF-IDF + Word2Vec + LDA + dependency parsing + NER pre-BERT), AI Engineer often doesn't know this (LLM-only). 2) Speech expertise — ASR (Whisper / Wav2Vec) + TTS (ElevenLabs / Tacotron) — exclusive NLP Engineer territory (AI Engineer rarely touches speech). 3) Linguistics intuition — NLP Engineer often has a computational-linguistics background (morphology / syntax / semantics formal training), AI Engineer usually a 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 months — add LangChain + agent frameworks). AI Engineer Senior → NLP Engineer — 3-6 months (classical NLP techniques + speech knowledge take time). ML Engineer Senior → NLP Engineer — 4-8 months. Reality 2026: the NLP Engineer title is giving way to AI Engineer in job postings (LLM hype), but classical NLP tasks (search / translation / extraction / speech) remain core production needs.

Decision tree for NLP techniques 2026: 1) Classical NLP only (no LLM) — best for: a) high-volume / low-latency production tasks (millions of requests per second — LLM API too expensive + slow), b) on-device / edge / offline constraints (mobile keyboards / IoT devices), c) deterministic / explainable requirements (legal / medical — need to show "how we arrived at the answer"), d) low-resource languages LLMs don't cover (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 (no LLM) — best for: production NLP tasks where latency / cost matter but high accuracy is needed. Stack: DeBERTa-v3 / RoBERTa / XLM-R fine-tuned + ONNX export + TorchServe / Triton. Examples: production NER (extract entities from millions of documents), text classification (sentiment / topic / intent), search ranking (cross-encoder reranker). Cost: $0.001-0.01 per request vs $0.01-1.00 for LLM API. Latency: 10-100ms vs 500-5000ms for LLM. 3) LLM zero-shot / few-shot (no training) — best for: a) prototyping (validate an idea in a day instead of a month), b) long-tail tasks (rare classes where fine-tuning isn't justified), 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 when zero-shot isn't enough + classical / small transformer isn't flexible enough. Stack: Llama 3.x / Mistral / Qwen + PEFT + Unsloth. Examples: domain-specific chatbot (medical / legal style + knowledge), specialised 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 for long-tail extraction (custom domain entities). d) Translation — neural MT (mBART / NLLB / Marian) for common pairs, LLM for low-resource or style-specific. Cost reality 2026: production system with 100M tokens/day. Classical-only: $0-50/month compute. Small transformer-only: $500-5,000/month (GPU). LLM API only: $50,000-300,000/month. Hybrid: $1,000-15,000/month (LLM only for hard cases, ~5-10% of requests). Default choice 2026: start with an LLM prototype (validates value), then optimise — move high-volume tasks to small fine-tuned transformers, keep LLM for long-tail.

Yes, 0% of NLP Engineer jobs are full-remote or hybrid. NLP work is fully cloud-based (training in cloud GPUs, models in Hugging Face Hub, datasets streaming, deployment in Kubernetes). Outsourcing shops (EPAM AI / Luxoft AI / Andersen / DataArt NLP Practice) — almost always remote on US NLP projects. Russian (Yandex Translate / Alice / Search / Sber.AI Salute Speech / Tinkoff chatbot / Just AI / ABBYY / STC) — hybrid or remote after probation. Russian banks — hybrid/office. International 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) — hybrid-standard. Relocant hubs for NLP: USA (Bay Area + NYC — major NLP labs density), UK (London — DeepMind NLP team), Canada (Toronto — Mila / Vector Institute), Germany (Berlin — DeepL HQ + Aleph Alpha), France (Paris — Hugging Face HQ + Mistral), Singapore, Israel (Tel Aviv — AI21 Labs). English for international NLP remote — must (all NLP literature + community + conferences ACL / EMNLP / NAACL are English-speaking).

Speech Engineer — sub-specialisation within NLP focused on 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 centres — accuracy mandate), real-time streaming ASR (WebRTC + chunked processing + endpoint detection), speaker diarisation (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 from 6 seconds of reference audio), prosody control (intonation + pacing + emotion), multilingual TTS, low-latency streaming TTS for real-time agents. Voice agents (rising 2024+): conversational AI with voice — combine ASR + LLM + TTS in a 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 knowledge (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 (premium for Russian — STT + TTS), Sber SaluteSpeech (banking voice + GigaChat voice), STC VoiceKit, VOSK (open-source offline). Pay: Senior Speech Engineer — premium over general NLP +15-25% thanks to rare-skill (audio processing skills + ML hybrid are rare). $7,000-12,000 Senior at Russian tech, $8,000-13,000 at speech-companies (AssemblyAI / ElevenLabs / Deepgram). Top $15,000-25,000+ Senior at 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 months.

At the top: Yandex, Sber.AI, ABBYY. Russian NLP heavyweights: Yandex — largest NLP employer in Russia (Translate — largest Russian NMT project; Search — search NLP pipelines; Alice — voice + dialogue; Yandex.GPT — LLM; Market — semantic search + product NER). Sber.AI (GigaChat NLP team + Salute Speech ASR / TTS + SberDevices voice assistants + banking text classification). ABBYY (legacy giant — document understanding + OCR + NER + relation extraction — classical NLP + modern transformers hybrid; FineReader engine). Tinkoff (chatbot + voice assistant + transaction categorisation + sentiment monitoring). Just AI (chatbot platform — JAICP — largest in Russia for enterprise). STC — Speech Technology Center (speech leader in Russia — call-centre analytics + biometric voice). VK / Mail.ru (Mail.ru Search + Search + AI assistants for VK Cloud / Calendar / Disk). iSpring (educational NLP). Gramota.ru (computational linguistics for Russian). Outsourcing shops: EPAM AI / NLP Practice (largest AI outsourcing in CIS for US NLP projects), Luxoft AI, Andersen AI, DataArt NLP, Itransition. International voice-AI companies (full-remote premium): 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 with 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 (top-tier salary): Google Search (largest NLP team in the world — search + Bard NLP), Meta AI Translation (NLLB project), Microsoft Translator, Apple Siri, Amazon Alexa, Apple Intelligence NLP team. Y Combinator NLP startups — premium 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 of morphology + syntax + semantics + pragmatics. Helps build intuition. Book: "Speech and Language Processing" Jurafsky / Martin (free online 3rd edition — bible of NLP, no need to read end-to-end, important chapters). 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 with BERTopic). Course: 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 on own classification dataset, fine-tune T5 on own summarisation task. 6) Sentence embeddings — sentence-transformers + BGE + E5. Build semantic search demo. Understand MTEB benchmark. 7) LLMs for NLP tasks — OpenAI / Anthropic APIs for few-shot classification / extraction / generation. Compare prompt-only vs fine-tuned approaches on same task. 8) Search & ranking — Elasticsearch / OpenSearch deep with hybrid (BM25 + dense). Set up production search demo. Cross-encoder reranking (BGE Reranker). 9) Speech track (optional but premium) — install Whisper / faster-whisper, build transcription pipeline + speaker diarisation (pyannote). Try ElevenLabs TTS + Coqui XTTS-v2 voice cloning. 10) Russian NLP specific (for Russian 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 (if cross-lingual interest) — XLM-R + mT5 + NLLB-200 (Meta — 200 languages translation). 14) Pet project portfolio: a) production NER pipeline with custom domain (e.g. job descriptions extraction); b) semantic search for an open dataset; c) Russian text classification fine-tuned ruBERT; d) voice agent demo (ASR + LLM + TTS in one pipeline). Document on GitHub + blog post. Russian courses: Karpov.Courses "NLP" track, Otus "NLP", MIPT DLSchool (NLP module), SkillFactory NLP, School21 (Sber) NLP track, DeepPavlov community courses. International (EN): 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). Must-read books: "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 for serious NLP track). Backend Senior / DS Middle + NLP interest → NLP Engineer Junior — 4-10 months.

0 active open NLP Engineer positions with explicit NLP specifics in our sample. The real pool is many times wider — many NLP roles are classified as general ML Engineer / Backend / AI Engineer (titles like "ML Engineer for chatbot" or "Backend Engineer with NLP focus"). True NLP-focused jobs in CIS + Europe are estimated at 200-800 positions active at any moment in 2026 (counting fuzzily classified ones). Geography: Russia / Poland / remote. Sources: hh.ru (especially Yandex / Sber.AI / ABBYY active), Habr Career, getmatch, Djinni, LinkedIn (huge international NLP segment via voice-AI companies + Big Tech), NoFluffJobs / JustJoin.it (Poland NLP-friendly), Telegram (@nlp_ru, @ml_jobs, @aijobs, @jobsforaiml, @ds_chat), career pages of EPAM AI Practice / Luxoft AI / Andersen / DataArt, specialised boards (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. The real market is broader thanks to the international remote segment (voice-AI + NLP companies — full-remote-friendly). Time to close a Senior NLP Engineer — 6-12 weeks (longer than general AI Engineer due to rare-skill combination — linguistics + ML + audio if speech track).

A Senior NLP Engineer owns the full NLP-product engineering cycle + technical leadership. Python deep + Backend Senior level: async / typing / FastAPI / pytest mastery. Linguistics intuition: morphology + syntax + semantics + pragmatics — at the level of "I understand why the model errs in this complex case". No formal linguistics degree needed, but baseline knowledge is critical. Hugging Face Transformers mastery: models (BERT family + T5 + LLM) + datasets + tokenizers + PEFT + Accelerate. Fine-tuning mastery (LoRA / QLoRA + full fine-tuning when justified). spaCy mastery: production NLP pipelines (NER + POS + dependency parsing + custom components + matchers), spaCy-transformers integration. Modern transformers: DeBERTa-v3 (best base for classification + NER 2026), T5 family, XLM-R / mT5 / NLLB (multilingual). Understand attention + tokenisation + 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 for complex ranking pipelines, cross-encoder rerankers (training own BGE Reranker variants). LLM integration for NLP tasks: prompt engineering for NER / extraction / classification, few-shot vs fine-tuned trade-off analysis, structured output (function calling). Speech mastery (if track includes): Whisper / Wav2Vec deep (fine-tuning for domain), pyannote speaker diarisation, 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 for low-resource languages, multilingual evaluation methodology. Russian NLP specifically (if Russia-focused): DeepPavlov mastery, Natasha advanced, custom Russian-specific tokenisation / morphology handling. Classical NLP knowledge: TF-IDF + BM25 internals, Word2Vec / GloVe / FastText, dependency parsing algorithms, CRF for sequence labelling — for understanding when classical beats 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 for translation), human eval methodology design. System design for NLP products: design NLP pipeline on a whiteboard under scale (100M+ texts/day), latency budgets (target P95 for real-time NLP), cost optimisation (cache + batch + smart routing). Soft: ADRs writing for 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 if speech). English for Senior+ MUST — NLP community / docs / papers / conferences ACL / EMNLP / NAACL are English-speaking. Optional bonus: open-source contributions to Hugging Face / spaCy / DeepPavlov / sentence-transformers — sharply increase market value. Papers at ACL workshops — premium for frontier-NLP companies (Cohere / Hugging Face / DeepL) hiring.

Data is collected automatically from 1000+ sources — Telegram job channels and job boards across CIS and Eastern Europe (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it, Pracuj.pl and others). Parsing runs 24/7, duplicates are filtered by description and URL, salary outliers are stripped. Detailed methodology — on the "How it works" page.