Staff Machine Learning Platform/Ops Engineer

Preply
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer (Forward Deployed)

HR System and Data Analyst - Maternity Cover

HR System and Data Analyst - Maternity Cover

HR System and Data Analyst - Maternity Cover

HR System and Data Analyst - Maternity Cover

HR System and Data Analyst - Maternity Cover

We power people’s progress.

At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalized learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact. So far, 90,000 tutors have delivered over 20 million lessons to learners in more than 175 countries. Every Preply lesson sparks change, fuels ambition, and drives progress that matters.


About the role

We’re hiring a Staff ML & Data Engineer to own and evolve Preply’s ML platform. You will design the systems that enable our ML teams to move from research to production seamlessly — at scale, across clouds, and with minimal friction.


This role blends strategic platform architecture with deep hands‑on engineering. You’ll partner with Engineering and ML leadership to define standards, introduce new tooling, and eliminate barriers to deploying reliable, cost‑efficient, and observable ML systems.


Your mission

  • Lead the design and implementation of Preply’s ML platform architecture: from experiment tracking and artifact management to scalable model deployment


  • Drive cloud‑native solutions for distributed training and inference, including GPU‑based training environments, autoscaling, and rollout strategies


  • Own technical direction for CI/CD for ML, incorporating testing, validation, and performance checks into every deployment pipeline


  • Embed observability best practices across ML workflows: metrics, alerts, drift detection, lineage


  • Act as a multiplier across engineering: mentoring, influencing standards, and de‑risking complex technical decisions


  • Partner with ML Leads and Product teams to align platform direction with experimentation velocity, cost‑efficiency, and user impact


  • Design and lead building ML services are modular, testable, and monitored from day one


  • Contribute to LLM platform capabilities, including RAG pipelines, latency‑optimized inference, and prompt experimentation frameworks



What we’re looking for

  • 8+ years of engineering experience in large‑scale Data/ML platforms, with proven ability to architect and scale production‑grade systems supporting dozens of ML use cases


  • Deep knowledge of cloud services and strong understanding of end‑to‑end ML workflows, including versioning, monitoring, and performance benchmarking


  • Experience with working with scientists and building enabling tools for them.


  • Excellent communication and influence across cross‑functional teams; motivated by product impact. Mentoring and coaching ML engineers.


  • Familiarity with LLM frameworks (LangChain, LlamaIndex), vector stores, and retrieval infrastructure



Why you’ll love it at Preply

  • An open, collaborative, dynamic and diverse culture;


  • A generous monthly allowance for lessons on Preply.com, Learning & Development budget and time off for your self‑development;


  • A competitive financial package with equity, leave allowance and health insurance;


  • Access to free mental health support platforms;


  • The opportunity to unlock the potential of learners and tutors through language learning and teaching in 175 countries (and counting!)



#LI-VL1


Our Principles

  • Care to change the world - We are passionate about our work and care deeply about its impact to be life changing.


  • We do it for learners - For both Preply and tutors, learners are why we do what we do. Every day we focus on empowering tutors to deliver an exceptional learning experience.


  • Keep perfecting - To create an outstanding customer experience, we focus on simplicity, smoothness, and enjoyment, continually perfecting it as every detail matters.


  • Now is the time - In a fast‑paced world, it matters how quickly we act. Now is the time to make great things happen.


  • Disciplined execution - What makes us disciplined is the excellence in our execution. We set clear goals, focus on what matters, and utilize our resources efficiently.


  • Dive deep - We leverage business acumen and curiosity to investigate disparities between numbers and stories, unlocking meaningful insights to guide our decisions.


  • Growth mindset - We proactively seek growth opportunities and believe today's best performance becomes tomorrow's starting point. We humbly embrace feedback and learn from setbacks.


  • Raise the bar - We raise our performance standards continuously, alongside each new hire and promotion. We build diverse and high‑performing teams that can make a real difference.


  • Challenge, disagree and commit - We value open and candid communication, even when we don’t fully agree. We speak our minds, challenge when necessary, and fully commit to decisions once made.


  • One Preply - We prioritize collaboration, inclusion, and the success of our team over personal ambitions. Together, we support and celebrate each other's progress.



Diversity, Equity, and Inclusion

Preply.com is committed to creating an inclusive environment where people of diverse backgrounds can thrive. We believe that the presence of different opinions and viewpoints is a key ingredient for our success as a multicultural Ed‑Tech company. That means that Preply will consider all applications for employment without regard to race, color, religion, gender identity or expression, sexual orientation, national origin, disability, age or veteran status.


#J-18808-Ljbffr

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.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.