Here is a blog on “What Startups Are Hiring Engineers With Strengths in Machine Learning / NLP (as of 2025)”. I cover a mix of global and India-based startups, give you roles they tend to hire for, and what to watch out for — especially useful if you’re looking for a job or internship in ML/NLP.
Why this is a good time to look for ML/NLP startup jobs
With the explosion of AI tools, large-language-model apps, voice assistants, chatbots, and data-driven products — demand for engineers who know machine learning and Natural Language Processing (NLP) has skyrocketed. Many young companies (startups) aim to build AI-first products, so they actively recruit ML/NLP engineers to build models, pipelines, and production-ready systems.
Some Startups Hiring ML / NLP Engineers
Here are a few startups (and smaller AI-first companies) that — as of 2025 — are publicly known to be hiring or expanding teams, offering opportunities for people with ML/NLP skills:
| Startup / Company | What they do / Focus Area | Typical Roles They Hire For |
|---|---|---|
| deepset — Berlin-based NLP startup | Builds enterprise-ready NLP platforms and tools (open-source + SaaS) for building real-world NLP apps. Wikipedia | ML Engineer, NLP / LLM Engineer, Backend + ML Integrations |
| Thinking Machines Lab — New AI startup (founded 2025) | Cutting-edge AI research and development (LLMs, human–AI collaboration, next-gen AI tools) Wikipedia+1 | ML/AI Researchers, LLM Engineers, AI System Engineers |
| Neysa — India-based AI infrastructure startup | Provides managed GPU cloud, MLOps and AI-infrastructure services for enterprises building GenAI/AI apps. Wikipedia+1 | ML Infrastructure / MLOps Engineers, AI Platform Engineers |
| Yellow.ai — Conversational AI & automation startup (serving global clients) | Builds chatbots, voice-bots and automation with support for many languages — heavy NLP + AI automation. Vocal | NLP Engineers, Chatbot / Voice-AI Engineers, Data/ML Engineers |
| AI / ML-specialist consultancies & niche AI firms (across healthcare, analytics, enterprise AI) — e.g. companies like Sravathi.ai (in pharma/chemical-AI R&D) | Use ML/NLP + domain expertise to build specialized AI solutions (not just generic products) sravathi.ai+1 | Data Scientists, NLP Scientists, ML Engineers (with domain knowledge) |
What Types of Roles Are in Demand
From the above — and from several job-market signals — the most in-demand roles in startups hiring ML/NLP engineers tend to include:
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NLP / LLM Engineers — building language models, chatbots, voice-AI, embeddings, semantic search, etc.
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ML Engineers / ML Infrastructure Engineers / MLOps — building end-to-end pipelines, deploying models, handling data, scalability, cloud infra.
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AI Researchers / Applied ML Scientists — experimenting with novel models, pushing boundaries of LLM or ML-based products (especially at AI-research startups).
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Full-Stack ML Engineers / AI-Product Engineers — combining backend/frontend + ML/NLP to build products (chatbots, recommendation engines, conversational AI, data-driven apps).
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Domain-Specialist ML Scientists — for startups working in specialized sectors (e.g. healthcare, pharma, satellite imagery, enterprise AI) combining domain knowledge + ML/NLP.
If you’re strong in Python, ML frameworks (like PyTorch, TensorFlow), have familiarity with NLP/LLMs, and can handle data + production pipelines — your skills are in high demand.
What to Look Out for — What Makes a Startup a Good Fit
When evaluating a startup for ML/NLP job, consider:
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Is it AI-first or AI-enabled? Startups where AI/NLP is central to product (chatbots, voice-AI, LLM-powered tools, AI infrastructure) are likelier to hire ML/NLP engineers than ones where AI is only a small utility.
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Stage & Funding — early- and growth-stage startups (seed to Series B/C) tend to have more open roles, flexibility, and larger growth potential.
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Infrastructure & Data Access — product-heavy AI or R&D startups give better exposure to large datasets, real-world deployment, and chance to build end-to-end systems.
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Opportunity to build or research — if you enjoy experimentation, research-heavy startups or those using LLMs/NLP are great for learning and impact.
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Your own interests & domain-fit — if you have interest in NLP, product-building, scalable systems, or domain specialties (healthcare, enterprise AI), match with relevant startup types.
Why This Matters — And What You Should Do Next
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The AI/NLP boom means jobs are available — both in established companies and in startups.
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For someone skilled in ML/NLP, startups often offer faster learning curves, broader responsibilities, and impact compared to large corporates.
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Startups are more likely to embrace recent ML/NLP tools, open-source frameworks, and cutting-edge models — giving you exposure to modern workflows and freedom to experiment.
If you’re job-hunting or preparing for a career in ML/NLP, it’s smart to:
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Keep an eye on startups working in NLP, voice-AI, GenAI, AI infra, or domain-specific AI.
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Build portfolio projects demonstrating ML/NLP + production mindset (data pipelines, model deployment, end-to-end systems).
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Be flexible about remote, contract, or hybrid roles — many AI startups offer remote or cross-country roles today.
Final Thoughts
Startups represent some of the most exciting and dynamic opportunities today for engineers with strengths in machine learning and NLP. Whether you want to build chatbots, deploy LLM-powered services, design AI infrastructure, or work on domain-specific intelligent systems — there are startups actively hiring, and growing fast.
