Hire NLP Developers
NLP is one of the hardest engineering disciplines to hire for. Most candidates look good on paper and underdeliver in production. Tecla rejects 97% of applicants so the only developers you see are the 3% who've already proven otherwise.
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Tecla: the AI talent partner for product teams
Before a candidate reaches you, they clear four bars with our technical team: AI-readiness, technical depth, soft skills, and English fluency. AI-readiness is about how engineers think and work with AI across their stack. The tools they default to, the architectures they propose, the way they reason through hard problems. We evaluate the whole engineer, not just the skill list.
AI-Readiness
Thinking and working with AI across tooling, architecture, and problem-solving.
Technical Depth
Reviewed by engineers who understand what production-ready actually means.
Soft Skills
How they communicate, integrate, and contribute beyond their individual output.
English Fluency
Assessed through live technical conversation with our team.
Tecla is not a generalist agency that added an AI tab to their website.
We are an AI-specialist talent network and every part of our vetting reflects that.
What our NLP Engineers build for you
Text Classification & Analysis
spaCy, Transformers, BERT, and custom models built into production classification and analysis features. Language understanding that works on messy real-world text, not just clean benchmark datasets.
Named Entity Recognition & Information Extraction
Entity extraction, relation extraction, and information extraction pipelines designed to identify key data accurately across document types, domains, and languages.
Model Fine-Tuning & Optimization
Transformer fine-tuning, inference optimization, and model compression for specialized domains. Few-shot learning and transfer learning applied when labeled data is scarce.
Production Deployment & Monitoring
Model performance monitoring, data drift detection, version management, and latency optimization. Text processing features that stay accurate and fast as your data volumes and vocabulary evolve.
NLP Developers ready to start
A sample of NLP engineers ready to interview this week.
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Builds conversational AI and semantic search products using state-of-the-art NLP models at high-growth SaaS companies. Specializes in retrieval-augmented generation, embeddings pipelines, and multilingual text processing. Has shipped NLP-powered features used by hundreds of thousands of users across HR, e-commerce, and productivity platforms.
Leads NLP platform development for enterprise clients in legal tech and healthcare, building large-scale text extraction, classification, and summarization systems. Deep expertise in fine-tuning transformer models and deploying them to production with low-latency requirements. Has led NLP engineering teams and established best practices for model evaluation and versioning.
Builds text classification and entity extraction systems. Learning production patterns for model deployment and monitoring. Has worked on content moderation and document processing projects.
Full-stack developer building NLP features into web applications. Has shipped text analysis and automated categorization tools. Works across frontend interfaces and backend NLP pipelines.
Works on question answering and semantic search systems. Experience with both traditional NLP and LLM-based approaches. Background in building text processing infrastructure for content-heavy applications.
Data scientist focused on text analytics and information extraction. Comfortable deploying NLP pipelines in cloud environments. Has built language understanding features for customer support and content platforms.
Experienced building sentiment analysis and document classification features. Specializes in domain-specific model adaptation and multilingual NLP. Has worked at SaaS companies processing millions of text documents.
Builds text classification and named entity recognition systems for production applications. Has deployed NLP models at scale for multiple industries. Strong background in transformer architectures and model fine-tuning.
Why hire NLP Developers through Tecla?
5-day average placement
We match you with qualified NLP developers in 5 days on average. Traditional recruiting firms take 42+ days and that is before the notice period.
Top 3% acceptance rate
Only 3 in 100 applicants make it through our vetting process. Every developer you meet has built production NLP systems with real accuracy requirements, not just completed a Hugging Face tutorial.
Zero timezone hassle
Full overlap with US business hours. When a model starts underperforming in production or a pipeline goes down, you get a response before the day ends, not the next morning.
The talent is there. You decide where they are based
US-based or nearshore, Tecla places senior NLP developers in both markets. Same expertise, same vetting, same standards. What changes is the route you choose and what you do with the difference.
Stop rehiring the same NLP role every 18 months
NLP knowledge compounds. A developer who understands your domain, your data, and your edge cases gets more valuable over time. Our 97% year-one retention means that institutional knowledge stays on your team.
Teams building with AI trust Tecla to hire
Eleven years, 500+ companies, 50,000+ vetted professionals. What they say about working with us.
Tecla is organized and provides a strong partnership experience. From hiring multiple engineers within weeks to maintaining consistent communication and feedback, they've shown real professionalism. Their follow-up and collaboration made the entire staffing process efficient and enjoyable.
Kristen Marcoe
I’m very happy with Tecla. Their support has improved our QA process, reduced bug reports by half, and made our onboarding process twice as fast. The team is responsive, cost-effective, and delivers high-quality candidates on time. Tecla has truly become a trusted extension of our internal hiring team.
Meit Shah
It was a pleasure working with Tecla. Their team quickly understood our hiring needs and found candidates that matched our technical requirements perfectly. Communication was seamless, and they were always quick to respond and deliver results. Tecla’s attention to quality made the entire experience smooth and efficient.
Mayya Bozhilova
Tecla successfully found candidates for our team and handled the entire process from scheduling to interviews. They were timely, responsive, and always kept communication flowing through email and messaging apps. I was really impressed with Tecla’s follow-up and thoroughness throughout the process.
Jessica Warren
Tecla's business model and team set our company up with engineers that we have the real possibility of working with long-term and can grow with our business. Tecla came in highly recommended, and their pace from introduction to engagement to presenting candidates was very fast.
David Bradley
Internally, we're moving much faster than we were without the remote engineers Tecla recruited for us and we've been able to implement far more features. Once we brought on our first full-time designer in South America, it made the quality of our user interface, product, and marketing efforts increase substantially.
Drew Batshaw
When we started our recruiting initiatives for LATAM developers, it was crucial for us to rely on a company that could provide deep local expertise to help us identify the best software developers in Latin America. The teams at Tecla really go the extra mile to understand our needs, which is what has made our partnership so successful!
Douglas Santos
Hire NLP Developers in 4 simple steps
Outline your needs
Share your use cases, languages, and production requirements. No lengthy forms. No back-and-forth for days. One focused call and we handle the rest.
Receive your shortlist within 3 to 5 business days
Every profile includes verified production experience, not self-reported skills. You are reviewing engineers who have built real NLP systems, not people who completed a Hugging Face course.
Interview top matches
See how they approach model selection, evaluation, and production deployment. You are evaluating fit, not teaching fundamentals. Candidates arrive briefed on your product context.
Start working together in week 2 to 3
We handle contracts, compliance, and paperwork across borders. You focus on onboarding them to your data, models, and engineering team workflow.
90-day replacement guarantee. If the match is not right, we find you another at no extra cost.
Two ways to hire NLP developers through Tecla
Select the option that matches your needs.
Staff Augmentation
Nearshore Teams
The real cost to hire NLP developers with Tecla
Whether you hire in the US or Latin America, Tecla's NLP developers bring the same production depth, the same overlap with your hours, and the same fluency. You choose where they are based.
US Salary Ranges
LATAM Salary Ranges
What is an NLP Developer?
An NLP developer is the engineer who makes your product understand human language. They build text classification systems, entity extractors, sentiment analyzers, and semantic search features using transformers, spaCy, BERT, and Hugging Face. Not a researcher. Not a data scientist who ran a sentiment notebook. The person you hire when keyword search is not enough and your product needs to actually understand what users are saying.
NLP developers sit between data science and software engineering. They're not pure researchers, but they understand language models well enough to build reliable production systems. Most work involves model selection, fine-tuning, pipeline design, and integrating NLP into applications.
They differentiate from general ML engineers through deep knowledge of language-specific challenges like ambiguity, context, and multilingual processing. Unlike researchers, they ship customer-facing features instead of publishing papers.
Companies hire NLP developers when moving beyond keyword search into language understanding. This happens after deciding NLP features make business sense but before knowing how to make them accurate, fast, and maintainable for production use.
Business Impact
When you hire an NLP developer, text processing stops being manual work and starts being automated. Most companies see faster document processing and better insights from unstructured data.
Automation at Scale: Text classification and entity extraction that processes thousands of documents daily. Tasks that took humans hours now finish in seconds with consistent accuracy.
Better User Experience: Search that understands intent instead of just matching keywords. Content recommendations based on semantic similarity. Features that feel intelligent because they actually understand language.
Data Insights: Sentiment analysis across customer feedback. Topic modeling that surfaces trends. Information extraction that turns unstructured text into structured data for analysis.
Your job description filters for NLP engineers who've built production language models, not just completed courses. Make it specific enough to attract people who've debugged model accuracy issues in production.
What Role You're Actually Filling
State whether you need someone to build text classification, entity extraction, question answering, or own your NLP strategy. Include what success looks like: "Building a classifier with 90%+ F1 score on production data" beats "working with text."
Give context about your data, languages, and what's not working. Are your current models underperforming on domain-specific text? Do you need multilingual support? Help candidates understand if this matches problems they've solved.
Must-Haves vs Nice-to-Haves
List 3-5 must-haves that truly disqualify. "Built production NLP models processing 10K+ documents daily" is specific. "Experience with text" is worthless. Include years with frameworks (spaCy, Transformers, BERT) and outcomes (improved accuracy, faster processing).
Separate required from preferred so strong candidates don't rule themselves out. Experience with specific transformer architectures might be nice, but if someone's shipped reliable NLP features and can learn new models, don't lose them.
How to Apply
Tell candidates to send a brief description of the most complex NLP system they built and what accuracy challenges they faced. This filters for people who've shipped real models.
Set timeline expectations: "We'll respond within 5 business days and schedule first interviews within 2 weeks" beats radio silence.
Good questions reveal how candidates think about model selection, evaluation, and production deployment. Not surface-level knowledge.
What it reveals: Understanding of classification approaches, data requirements, and common NLP challenges. Listen for specific decisions about model architecture, handling class imbalance, evaluation metrics.
What it reveals: Hands-on debugging beyond "retrain the model." Look for discussion of analyzing error patterns, identifying data drift, testing domain adaptation, measuring improvement properly.
What it reveals: Whether they own outcomes or execute tasks. Listen for ownership of metrics like precision, recall, F1 score, latency. Strong candidates explain error analysis and model iterations.
What it reveals: How they debug complex systems and learn from failures. Look for honesty about what went wrong, specific debugging techniques, and improvements made.
What it reveals: Strategic thinking about limited data scenarios. Watch for discussion of transfer learning, data augmentation, few-shot approaches, when to use pre-trained models.
What it reveals: Collaborative problem-solving and communication style. Listen for partnership mindset, not gatekeeping. Strong candidates educate stakeholders about realistic expectations.
Neither answer is wrong, but reveals their natural orientation. Engineers who prefer greenfield work excel at building new classification systems and pipelines. Those who enjoy optimization and maintenance thrive improving models already serving users. Strong candidates are honest about what energizes them and what feels like a grind.
Frequently asked questions
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See who is available for your stack this week
No commitment. A 30-minute call and a shortlist in 5 days. 90-day guarantee if the fit is not right.