Machine Learning Engineer (Applied AI) (100% Remote in EMEA)

Testlio
Manchester
1 month ago
Create job alert
About the job

Testlio’s fully managed crowdsourced testing platform, powered by our proprietary intelligence technology – LeoAI EngineTM, integrates expert, on‑demand testers directly into your release process. Ship faster and more confidently with global coverage across 600,000+ devices, 800+ payment methods, 150+ countries, and 100+ languages. To learn more, visit www.testlio.com.


We are hiring a Staff Machine Learning Scientist (Applied AI) to help design, build, and scale Testlio’s next generation of AI‑powered data products. You’ll join our product team and will apply advanced machine learning, data science, and statistical methods to transform the rich data from our software testing platform into actionable insights for our customers. Your work will directly influence how global engineering and product teams understand product quality, user experience, and business impact. This is a hands‑on, high‑impact opportunity to shape the future of AI at Testlio while working in a remote, collaborative, and fast‑paced environment.


Why will you love this job?

  • Build from the ground up. You’ll be part of the team shaping Testlio’s new AI product line, creating real‑world solutions that customers will use to make smarter product decisions.
  • High‑impact data. Our platform generates unique and complex datasets from software testing at scale, giving you access to rich, real‑world data to design and deploy meaningful models.
  • Experiment + innovate. You’ll have freedom to test, prototype, and deploy new approaches in NLP, predictive modeling, and data‑driven insights.
  • Cross‑functional collaboration. You’ll work closely with engineers and customer‑facing teams to ensure your models deliver measurable business value.

Why will you love being a part of Testlio?

  • Great Culture : Testlio is a female‑founded company, and half our team identifies as women. We’re proud of our inclusive, purpose‑driven culture where people genuinely enjoy collaborating. As part of our team (we call ourselves TestLions), you’ll help create exceptional digital experiences for our customers, while also contributing to our freelance network.
  • Remote Work : Our culture is built around remote work. We’ve created systems to allow us to successfully work together asynchronously as a fully remote and globally distributed team. Testlio provides the tools and guidance for everyone to succeed in their careers in a fully remote setting. Our working style encourages everyone to make decisions, communicate effectively, and work at a sustainable pace.
  • Investment in You : Your growth and well‑being matter to us. You’ll have flexible paid time off—including national holidays, personal days, and sick days—plus stock options so you can grow with Testlio. We also provide a $300 annual learning stipend to support your personal and professional development.
  • Winning Business : Testlio is growing, profitable, and cash‑strong. We are leading our industry with exceptional clients who provide us with a high NPS score and a 4.7 rating on G2. Our business model is global, enterprise, and subscription‑based. Several of our largest clients have been with us for 7+ years, and many spend $500K+ / year with Testlio.

What would your day look like?

  • Partner with engineering leaders to define, design, and deliver AI Data products.
  • Explore and model complex datasets from Testlio’s platform using statistical, ML, and deep learning techniques.
  • Prototype and validate models, then work with engineering to deploy them into production at scale.
  • Research and implement techniques in areas like NLP, anomaly detection, recommendation systems, and predictive analytics.
  • Translate raw outputs into clear, actionable insights that are easy for customers to understand and use.
  • Measure and improve model performance continuously to ensure accuracy, fairness, and scalability.
  • Contribute to building Testlio’s data science practice — influencing standards, tools, and best practices.

What do you need to succeed?

Technical Skills



  • Advanced degree (Master’s or PhD) in Computer Science, Data Science, Statistics, or a related field.
  • 10+ years applying end‑to‑end machine learning and statistical modeling solutions to real‑world problems, ideally in SaaS or data products.
  • Strong Python skills and experience with deep learning frameworks such as PyTorch and TensorFlow.
  • Hands‑on experience with NLP, predictive modeling, recommendation systems, anomaly detection, and training ML and deep learning models from scratch.
  • Expertise in data wrangling, feature engineering, and managing large, complex datasets.
  • Knowledge of Large Language Models (LLMs) and Small Language Models (SLMs), including architecture, training, fine‑tuning (LoRA, QLoRA, SFT), and deployment strategies.
  • Proven experience designing, building, and maintaining end‑to‑end ML pipelines and MLOps frameworks, including model training, deployment, monitoring, and lifecycle management.
  • Hands‑on experience designing, deploying, and maintaining AI agents, including multi‑agent systems, in production with robust APIs and error handling.
  • Proficiency with SQL, data visualization tools, and cloud platforms such as AWS or Azure.

Human Skills



  • Curiosity + creativity. You’re eager to experiment, learn, and push the boundaries of what’s possible with data.
  • Business orientation. You can translate technical outputs into meaningful customer value.
  • Collaborative spirit. You enjoy working across teams to deliver impact.
  • Adaptability. You thrive in fast‑changing environments where priorities evolve as we scale.
  • Mentorship mindset. You enjoy sharing knowledge and uplifting teammates.
  • Growth mindset. You continuously refine your craft, stay current with advances in AI/ML, and seek feedback to improve.

What is the application process?

  • Application
  • Recruiter interview
  • TestGorilla assessment
  • ~3 Team and Stakeholder interviews, inclusive of skills demonstration
  • Reference checks
  • Offer

Diversity and Inclusion


Testlio is an equal‑opportunity employer deeply committed to creating an inclusive environment for people of all backgrounds and identities. We are female‑founded, and 46% of our team members identify as women. For more information, see the DEI section of our website.


#LI-Remote


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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 Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.