National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Machine Learning Engineer – NLP

NLP PEOPLE
London
7 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer NLP Specialist

Machine Learning Engineer - West London

Machine Learning Engineer

Machine Learning Engineer (AGI ), AGI Vertical Service Inference & Engine

Machine Learning Engineer

Machine Learning Engineer

In a Nutshell:

Make sure to read the full description below, and please apply immediately if you are confident you meet all the requirements.We are looking for a Machine Learning Engineer to join our 15-month-old, rapidly growing, psychological AI startup to build robust AWS infrastructure solutions for the Boon Platform which tackles complex questions about human behaviour, and in doing so, tackle some of the most challenging issues of our time. You will be working closely and collaboratively with our Lead Engineer, Lead Data Scientist, Lead Front-End Developer, and Lead Behavioural Scientist.

Why This Role Is Important to Boon’s Work:

Our focus is to use cutting edge data science and behavioural science techniques to answer complex questions about human behaviour. We work with non-profits, municipalities, strategy consultancies, and businesses to solve business and societal challenges. At our core, we’re a company founded on the premise that data can be used for social good, and we take on as many projects as possible aligned to that mission.

We are an early stage startup and are looking for a Machine Learning engineer to enhance our Boon Platform by integrating cutting-edge NLP technologies and supporting our data-driven applications through robust software engineering practices.

Once You Are Here, You Will:

Design and build scalable NLP models using state-of-the-art techniques and frameworks.

Stay up-to-date with the latest advancements in NLP and machine learning, and apply new methodologies to improve existing models.

Deploy data science models on scalable AWS cloud infrastructures, ensuring best practices for security and performance.

Assist in Infrastructure as Code initiatives using Terraform.

Write clean, maintainable Python code for data science software, ensuring high standards of code quality and maintainability.

Continuously monitor and improve the performance of data science models in production.

Work closely with cross-functional teams including behavioural scientists, data scientists, software engineers, and product managers to deliver end-to-end solutions.

What Do We Offer You:

Boon is unique. It’s a place where you can solve complex, diverse challenges about human behaviour, have a societal impact, and work with thoughtful, collaborative people who are deeply passionate about their crafts. We were founded out of a passion for using data for social good, and for merging the art of psychology with the science of data. We encourage everyone who works with us to be themselves, to bring their most creative selves to the table, and we promise to respect the work-life balance, autonomy, and collaboration you need.

For our Machine Learning Engineer, we offer:

Flexible working with benefits to make flexible working truly work for you.

Sponsored trainings so you can continue to learn and grow.

Shaping purpose-driven insights and technological innovations, including climate-tech hackathons.

Working with companies who excel in their craft – whether it be superhero movies, luxury brands or sustainability organisations.

Crafting how companies think about human beings and how they measure behaviour.

To work with optimistic, open-minded people who value creativity, empathy, and a good laugh.

Relevant Experience & Mindset:

Minimum of 2+ years’ experience in developing data science models, including NLP models, and deploying them in a production environment.

Bachelors degree in computer science, data science, mathematics, statistics, engineering or related field.

Experience with NLP libraries and frameworks like spaCy, NLTK, Gensim, or Hugging Face Transformers.

Deep expertise in cloud computing platforms such as AWS, with advanced knowledge of services like ECS, EKS, S3, VPC, Lambda, Networking, and IAM.

Experience with infrastructure as code tools (Terraform) and containerisation technologies (Docker, Kubernetes).

Expertise in software development practices such as version control, code reviews, software design patterns, and CI/CD practices and tools.

Proficiency in writing clean, robust, and scalable Python code for backend functionality.

Team player who is proactive and resilient.

A passion for social good.

Boon’s Mission Statement:

We are an equal opportunity employer. As an ethnically and cognitively diverse, female-founded team, diversity, inclusivity, and social impact are part of our DNA. We were founded with the commitment to using data and technology to drive positive societal change, and that is reflected in the clients we work with. We do not work with oil and gas companies, nor with political parties on political elections, and we evaluate every client project to ensure its outcome does not result in societal harm. We collaborate with non-profits and intend to leverage our most cutting-edge technology on for-good use cases.

Company:Boon

Qualifications:Language requirements:Specific requirements:Educational level:Level of experience (years):Senior (5+ years of experience)

Tagged as: Industry, Machine Learning, NLP, United Kingdom

#J-18808-Ljbffr

National AI Awards 2025

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.

How to Get a Better Machine Learning Job After a Lay-Off or Redundancy

Redundancy in machine learning can feel especially frustrating when your role was technically advanced, strategically important, or AI-facing. But the UK still has strong demand for machine learning professionals across fintech, healthtech, retail, cybersecurity, autonomous systems, and generative AI. Whether you're a research-oriented ML engineer, production-focused MLOps developer, or applied scientist, this guide is designed to help you bounce back from redundancy and find a better opportunity that suits your goals.

Machine Learning Jobs Salary Calculator 2025: Figure Out Your True Worth in Seconds

Why last year’s pay survey is useless for UK ML professionals today Ask a Machine Learning Engineer wrangling transformer checkpoints, an MLOps Lead firefighting drift alarms, or a Research Scientist training diffusion models at 3 a.m.: “Am I earning what I deserve?” The honest answer changes monthly. A single OpenAI model drop doubles GPU demand, healthcare regulators release fresh explainability guidance, & a fintech unicorn pays six figures for vector‑search expertise. Each shock nudges salary bands. Any PDF salary guide printed in 2024 now looks like an outdated Jupyter notebook—missing the gen‑AI tsunami, the surge in edge inference, & the UK’s new Responsible‑AI framework. To give ML professionals an accurate benchmark, MachineLearningJobs.co.uk distilled a transparent, three‑factor formula that estimates a realistic 2025 salary in under a minute. Feed in your discipline, UK region, & seniority; you’ll receive a defensible figure—no stale averages, no guesswork. This article unpacks the formula, highlights the forces driving ML pay skyward, & offers five practical moves to boost your value inside the next ninety days.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.