At a Glance
- Tasks: Lead the development of a cutting-edge ML platform that transforms education globally.
- Company: Join Preply, a unicorn Ed-Tech company revolutionising learning experiences.
- Benefits: Enjoy competitive pay, equity, health insurance, and a generous learning allowance.
- Other info: Collaborative culture focused on growth, diversity, and making a difference.
- Why this job: Shape the future of AI in education and make a real impact on learners worldwide.
- Qualifications: 9+ years in engineering with expertise in large-scale ML and cloud-native architecture.
The predicted salary is between 75600 - 92400 £ per year.
At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact. We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we’re only getting started.
As a category-defining company, we’re shaping what the future of learning looks like at global scale. Every Preply lesson sparks change, fuels ambition, and drives progress that matters. Joining Preply means helping define the future of education at global scale, and building something that truly matters for millions of people, every day.
Preply is scaling an AI-powered learning platform used by millions of learners and tutors worldwide. As ML and GenAI become increasingly central to our product, marketplace, and operational systems, we’re looking for a Staff ML Ops Engineer to define and evolve the platform that enables machine learning at company scale. This is a senior technical leadership role for an engineer who can combine deep hands-on expertise with platform vision.
You will help define the current and future direction of Preply’s ML platform, set technical standards, and design the systems that allow ML teams to move from research to production quickly, safely, and cost-efficiently. You’ll partner closely with Applied Science, Data, Product, and Engineering leadership to build a scalable, secure, and observable ML platform that powers multiple business lines. Your impact will come not only from the systems you design, but from the standards you set, the engineers you mentor, and the technical decisions you help de-risk across the organization.
Why is this role important: Preply’s next stage of growth depends on making AI a scalable company-wide capability, not a collection of isolated models and experiments. This role will define how ML systems are built, deployed, monitored, and improved across the company. You’ll shape the platform that powers personalized learning, smarter experiences for tutors and learners, marketplace intelligence, content generation, automation, and future GenAI products. The goal is to make it dramatically easier for teams to turn ML ideas into reliable, secure, cost-efficient product experiences at a global scale.
What you’ll be doing:
- Define the technical vision and roadmap for Preply’s ML platform, ensuring it can support growing ML and GenAI adoption across multiple teams, products, and business lines.
- Lead the architecture of platform capabilities across the full ML lifecycle: experimentation, feature engineering, artifact management, training, deployment, monitoring, retraining, and governance.
- Design cloud-native infrastructure for distributed training and inference, including GPU-based environments, autoscaling, workload isolation, rollout strategies, and cost optimization.
- Set the technical direction for CI/CD for ML, embedding testing, validation, security, performance checks, and release confidence into deployment pipelines.
- Establish observability standards for ML systems, including model metrics, service health, alerts, drift detection, data quality, lineage, and business-impact monitoring.
- Lead the evolution of Preply’s GenAI and LLM platform capabilities, including building LLM Gateway services, vector retrieval infrastructure, prompt experimentation, evaluation frameworks, latency-optimized inference, and reliable model-serving patterns.
- Partner with Applied Science and Data leads, Product leaders, and Engineering teams to align platform investments with experimentation velocity, cost efficiency, operational reliability, and user impact.
- Design platform abstractions, internal libraries, templates, and self-service tooling that help ML Scientists and engineers move faster without compromising reliability or security.
- Act as a technical multiplier across engineering by mentoring senior engineers, influencing architecture, raising standards, and guiding teams through complex platform decisions.
- Identify and eliminate bottlenecks in the path from ML research to production, making the platform easier, safer, and more efficient for all ML-powered product development.
What you need to succeed:
- 9+ years of engineering experience, with significant depth in large-scale ML, data, infrastructure, or platform systems.
- Proven ability to architect and scale production-grade ML platforms that support many teams, workflows, and ML use cases.
- Deep understanding of cloud-native architecture and end-to-end ML workflows, including experimentation, feature management, model versioning, training, deployment, monitoring, performance benchmarking, and lifecycle management.
- Strong hands‑on experience with cloud platforms such as GCP or AWS, Kubernetes, distributed compute, CI/CD, observability, and infrastructure‑as‑code practices.
- Experience building enabling tools and platform capabilities for Applied Scientists, Data Scientists, and engineering teams.
- Strong technical judgment and the ability to make pragmatic architecture decisions across reliability, scalability, security, cost, and developer experience.
- Excellent communication and influence skills, with experience aligning cross‑functional stakeholders and translating platform strategy into execution.
- Demonstrated ability to mentor engineers, raise engineering standards, and multiply the impact of teams around you.
- Product‑impact mindset: you care about building platform capabilities that accelerate experimentation, improve user experiences, and unlock measurable business value.
- Familiarity with LLM frameworks and GenAI infrastructure, such as LangChain, LlamaIndex, vector stores, retrieval systems, prompt evaluation, model serving, or LLM observability.
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!).
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.
Staff Machine Learning Ops Engineer Location: London employer: Preply Inc.
At Preply, we pride ourselves on fostering a vibrant and inclusive work culture that champions collaboration and innovation. As a Staff Machine Learning Ops Engineer in London, you'll enjoy a competitive financial package, generous learning allowances, and access to mental health support, all while contributing to a mission that transforms education globally. With ample opportunities for professional growth and the chance to mentor fellow engineers, you'll be part of a dynamic team dedicated to making a meaningful impact in the world of learning.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Ops Engineer Location: London
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We think you need these skills to ace Staff Machine Learning Ops Engineer Location: London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Preply Inc.. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Preply Inc.
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Preply Inc.!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.