Natural Language Processing (NLP) Engineer

Your Personal AI
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

GenAI & NLP Data Scientist (Full‑Stack)

Senior Big Data Scientist - NLP, ML & Distributed Systems

Big Data Scientist

NLP / Machine Learning Data Scientist

Full-Stack Data Scientist: NLP & Production ML

Data Science Specialist – AI Trainer

Natural Language Processing (NLP) Engineer at Your Personal AI

Your Personal AI is seeking a talented Natural Language Processing (NLP) Engineer to join our AI Research and Development department. As an NLP Engineer, you will play a key role in developing cutting-edge algorithms and models to enhance our AI technology.

  • Collaborate with a team of researchers and developers to design and implement NLP solutions

  • Utilize machine learning techniques to improve language understanding and processing

  • Conduct experiments and analyze data to optimize NLP algorithms

  • Stay up-to-date with the latest advancements in NLP and AI technologies

If you are passionate about NLP and have a strong background in machine learning and data analysis, we would love to hear from you. Join us at Your Personal AI and be part of a dynamic team that is shaping the future of artificial intelligence.



Job Requirements for Natural Language Processing (NLP) Engineer at Your Personal AI

Thank you for your interest in the NLP Engineer role at Your Personal AI in the AI Research and Development department. To ensure we find the best candidate for this position, please review and include the following job requirements in your job posting:

  • Bachelor's degree in Computer Science, Engineering, or related field

  • Proven experience in developing NLP algorithms and models

  • Familiarity with machine learning techniques and frameworks

  • Proficiency in programming languages such as Python, Java, or C++

  • Strong analytical and problem-solving skills

  • Excellent communication and teamwork abilities

  • Ability to work independently and meet project deadlines

If the job requirements are not met, we kindly ask you to revise the job posting accordingly. Thank you for your attention to this matter.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.