NLP

Connecting top NLP talent with extraordinary opportunities

Whether you’re building a cutting-edge NLP team or hoping to join one, DeepRec.ai connects the best people with the most exciting opportunities on the market.

From biotech to financial services, we partner with pioneering companies to recruit exceptional NLP engineers, researchers, and technical leaders. Let us know what you’re looking for, and our team will make it happen.

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The Rise of NLP

The rise of Natural Language Processing (NLP) has taken the world by storm, and we’re here for it. From predictive text and chatbots to fully-fledged virtual companions like Siri and ChatGPT, NLP has moved machine learning into the mainstream.

The latest phase of NLP adoption is driving demand for domain-specific expertise. The talent pool is short, and projects are time-sensitive, leading to high premiums and fierce competition (like most areas of deep tech).

Where is NLP Heading?

Digital Signal Processing, Exploratory Data Analysis, Algorithm Development, and Microsoft Azure Machine Learning are among the fastest-growing skills inside this talent pool, with specialists commanding more market influence than ever.

Alternative approaches to recruitment are essential in a space that’s short on skills, particularly when you’re competing against big budgets.

Through targeted event initiatives, podcast series, meetups, and more, DeepRec.ai’s natural language processing consultants have been busy building a global community of technologists.

This approach creates exclusive access to passive candidates, the best talent that can’t be found through traditional recruitment.

Whether you’re scaling a product team or making a mission-critical leadership hire, we help you move fast and land the right person at the right time.

Our consultants have the technical expertise, talent market insight, and commitment to diverse hiring practices needed to expand your search in the right direction.

How Do I Hire NLP Talent?

Hiring NLP specialists typically requires a granular understanding of role requirements, technical understanding, market insight, and access to a well-networked community. Some of the key ingredients of a competitive recruitment process include:

A strong employer value proposition – Top NLP candidates are drawn to cutting-edge projects. They're looking for the latest tech, training, investment, and buy-in. Does your brand story meet expectations?
A clearly defined role scope – Role clarity is essential in a space that evolves quickly. Set clear expectations, responsibilities, and achievable goals to attract value-aligned candidates.
Benchmarked remuneration packages – Competitive salaries are critical in high-demand, low-supply markets like natural language processing. Are your offers aligned with current market data? This should include equity, bonuses, and benefits.
A streamlined interview process – Multi-stage interviews are infamous for driving up candidate dropout rates. The fewer stages you have (while still assessing the aspects that matter), the better the chance of securing your ideal talent.

If you'd like DeepRec.ai to build you a bespoke market guide, including competitor analysis, salary data, and talent insights, then we're happy to help. Let the team know what you'd like to explore, and we'll tailor the content to your needs: Enquire.

The roles we recruit for in NLP include:

Head of NLP
Senior NLP
NLP Engineer
Senior Machine Learning Engineer, NLP
Machine Learning Engineer NLP
NLP Scientist
NLP Researcher

NLP CONSULTANTS

Meet Anthony

Anthony Kelly

Co-Founder & MD EU/UK

Meet Hayley

Hayley Killengrey

Co-Founder & MD USA

Meet Jonathan

Jonathan Harrold

Consultant - Germany

Meet Benjamin

Benjamin Reavill

Consultant - US

LATEST JOBS

New York, United States

Machine Learning Engineer (NLP)

Permanent$140000 - $200000 per annum

Machine Learning Engineer (NLP) About the Company This early-stage environmental intelligence startup is building next-generation AI systems that help global organisations understand and plan for water-related risks. Their platform combines deep learning with physics-based modelling to generate high-resolution insights for some of the world’s largest infrastructure operators, consumer brands, and investors. Backed by leading scientific minds across climate, hydrology, and machine learning, the company is now expanding its capabilities by developing a new social risk function that captures the human, regulatory, and community dynamics that shape water outcomes around the world.Why JoinJoin a team pushing the boundaries of environmental intelligence, combining physical and social risk modelling into a unified AI platform. Work with world-class researchers, publish meaningful science, and help deliver tools with tangible global impact.Pioneer a new capability: You’ll be the first ML engineer dedicated to modelling social, political, and reputational water risk.Cutting-edge work: Blend NLP, LLMs, graph intelligence, and geospatial modelling into a real, production platform.Genuine impact: Your models will inform global water stewardship decisions across high-risk regions.Interdisciplinary collaboration: Work alongside scientists and researchers across climate, hydrology, and social systems.Early-stage ownership: Build from first principles in a fast-moving, mission-driven startup with strong early traction.What You’ll DoBuild NLP, LLM, and multi-modal pipelines to analyse community, regulatory, media, and public-sentiment signals — including stance detection, topic/event clustering, and stakeholder network mapping.Fuse unstructured social data with geospatial and physical-risk datasets to generate unified risk insights for real-world decision-making.Partner with climate and domain scientists to translate social signals into actionable risk metrics, contributing to both product development and peer-reviewed research.Deploy scalable, interpretable ML systems into production via APIs and platform infrastructure.What You Bring3 years building applied ML/NLP systems, ideally across text, geospatial, or social-network data, including sentiment/stance modelling and multi-source pipelines.Strong Python plus experience with PyTorch/TensorFlow, SQL, and modern LLM tooling (Hugging Face, LangChain, OpenAI APIs).Skilled with entity extraction, topic modelling, network/graph analysis, and data sourcing or weak supervision in multilingual environments.Passion for climate, water, or environmental risk, and comfortable working in an early-stage, collaborative, low-ego environment.Nice to HavePhD / Postdoc with track record of pace and quality of publicationsGraph ML experience or multi-modal fusion (text geospatial).LLM fine-tuning for domain-specific tasks.Deployment experience with FastAPI, Docker, or similar frameworks.Background or exposure to environmental science, hydrology, or social-data analysis.

Benjamin Reavill

Posted about 2 hours ago

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San Francisco, California, United States

Senior Agentic AI Engineer

Permanent$200000 - $250000 per annum

Senior Agentic AI EngineerA frontier AI company is building systems that can act in the physical world, experimenting, engineering, and executing multi-step processes with real-world constraints. Backed by major research funding and operating at the edge of physical-AI innovation, they’re creating capabilities that don’t exist anywhere else. Join to work from first principles, own high-impact systems end-to-end, and help define how agentic AI will operate complex workflows in the real world. Why This Role MattersBuild agent systems that plan, execute, and recover across intricate engineering workflowsShape foundational behaviour patterns for next-gen LLM tool-useJoin early enough to influence architecture, culture, and performance standardsWork on problems that sit far beyond typical “LLM app” engineeringWhat You’ll DoDevelop planners, state machines, and tool-calling flows using frameworks like LangGraphCreate schemas, action definitions, and cross-tool interfaces for reliable, traceable executionBuild error-handling, timeouts, retries, rollbacks, and replay mechanismsPartner with ML, infra, and systems teams to integrate agents into real engineering toolchainsWhat You BringStrong experience with agent systems, structured tool calling, or orchestration frameworksDeep intuition for schemas, deterministic execution, and multi-step workflow designAbility to model failure modes, edge cases, and safe interactions in complex systemsComfort working across AI, systems engineering, and specialised domain tools in a high-precision environment

Benjamin Reavill

Posted 2 days ago

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Remote work, United States

AI Evaluation Engineer

Permanent$180000 per annum

AI Evaluation Engineer$180,000 Remote (US-based)Are you passionate about shaping how AI is deployed safely, reliably, and at scale? This is a rare opportunity to join a mission-driven tech company as their first AI Evaluation Engineer, a foundational role where you’ll design, build, and own the evaluation systems that safeguard every AI-powered feature before it reaches the real world.This organization builds AI-enabled products that directly helps governments, nonprofits, and agencies deliver financial support to people who need it most. As AI capabilities race forward, ensuring these systems are safe, accurate, and resilient is critical. That’s where you come in.You won’t just be testing models, you’ll be creating the frameworks, pipelines, and guardrails that make advanced LLM features safe to ship. You’ll collaborate with engineers, PMs, and AI safety experts to stress test boundaries, uncover weaknesses, and design scalable evaluation systems that protect end users while enabling rapid innovation. What You’ll DoOwn the evaluation stack – design frameworks that define “good,” “risky,” and “catastrophic” outputs.Automate at scale – build data pipelines, LLM judges, and integrate with CI to block unsafe releases.Stress testing – red team AI systems with challenge prompts to expose brittleness, bias, or jailbreaks.Track and monitor – establish model/prompt versioning, build observability, and create incident response playbooks.Empower others – deliver tooling, APIs, and dashboards that put eval into every engineer’s workflow. Requirements:Strong software engineering background (TypeScript a plus)Deep experience with OpenAI API or similar LLM ecosystemsPractical knowledge of prompting, function calling, and eval techniques (e.g. LLM grading, moderation APIs)Familiarity with statistical analysis and validating data quality/performanceBonus: experience with observability, monitoring, or data science tooling

Benjamin Reavill

Posted 11 days ago

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Boston, Massachusetts, United States

Machine Learning Engineer (LLM)

PermanentSalary Competitive DOE

Machine Learning Engineer (LLM) $200,000 - $220,000+ (DOE) Boston OR Berkeley, 2-3 days per week in-office We’re working a fast-growing AI company on a mission to automate complex workflows in the financial services sector, starting with insurance. Their technology leverages cutting-edge AI to simplify high-value processes, from multi-turn conversations to full workflow automation. As an ML Engineer within LLMs, you’ll be building and scaling advanced AI systems that power intelligent, multi-agent workflows. You’ll take ownership of designing, fine-tuning, and productionizing large language models, integrating them with backend systems, and optimizing their performance. You’ll collaborate closely with data science, DevOps, and leadership to shape the AI infrastructure that drives the company’s automation solutions. What You’ll Do:Build, fine-tune, and productionize large language model (LLM) pipelines, including PEFT, RLHF, and DPO workflows.Develop APIs, data pipelines, and orchestration systems for multi-agent, multi-turn AI conversations.Integrate models with backend services, including voice orchestration platforms and transcript generation.Optimize model usage and efficiency, transitioning from external APIs to in-house solutions.Collaborate cross-functionally with data scientists, DevOps, and leadership to deliver scalable machine learning solutions. What We’re Looking For:Essential Skills & Experience:Strong proficiency in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).Hands-on experience fine-tuning and training LLMs.PEFT, DPO, Prefence Optimization, post-training, supervised fine tuning, RLHFFamiliarity with AWS suite and deploying ML models to production.Ability to reason deeply about ML principles, architectures, and design choices.Knowledge of multi-agent orchestration and conversational AI systems.Desirable Skills & Experience:Background in voice AI, speech-to-text, or text-to-speech systems.Exposure to financial services or insurance applications.Familiarity with optimizing models for long-context scenarios. If you’d like to hear more, please apply or get in touch!

Benjamin Reavill

Posted about 2 months ago

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