Staff Machine Learning Platform/Ops Engineer

Preply
BarcelonaPosted 27 March 2026

Job Description

Staff Machine Learning Platform/Ops Engineer 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 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. 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 http://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. - ... (truncated, view full listing at source)
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