Jobs at Preply

Edit and deliver polished video content for paid media, brand campaigns, social channels, and product videos. Responsibilities include campaign storytelling, channel-specific editing, color grading or audio mastering, motion graphics adaptation, localization, light compositing, quality control, and technical delivery using Adobe Creative Suite. The role collaborates with designers, motion artists, copywriters, and photographers while improving post-production workflows and maintaining brand consistency.

Own and improve the end-to-end lesson experience by analyzing customer and operational data, refining scalable support workflows, identifying product and policy improvements, and leading cross-functional initiatives. The role manages CX metrics, supports launches and change management, influences Product and Supply Operations roadmaps, and tracks measurable outcomes related to satisfaction, efficiency, and policy adoption.

Conduct outbound prospecting through LinkedIn, cold calls, email, video, and other channels. Qualify prospects using consultative conversations, research target companies, maintain accurate CRM data, schedule discovery calls, handle objections, and nurture leads. Collaborate with Account Executives to build the sales funnel and target high-potential accounts while developing expertise in Preply Business and B2B sales.

Build and improve Preply’s copy operations system by managing workflows, prioritization, capacity planning, AI-enabled processes, automations, dashboards, and performance insights. The role supports multilingual copy operations, improves adoption of new tools, and helps teams use AI responsibly. It requires practical process-improvement skills, comfort with data and experimentation, strong organization, cross-functional communication, and sound judgment around quality, privacy, and human oversight.

Develop marketing optimization models across creative performance, conversion-lag prediction, value-based bidding, and audience uplift modeling. Define metrics, apply causal inference and experiment design, improve channel ROI, and collaborate with Applied AI, Data Engineering, MarTech, marketing measurement, tracking, and CRM teams. Build scalable pipelines and automate detection, reporting, and optimization actions.

Develops data science and machine learning solutions for paid marketing optimization. Responsibilities include creative performance modeling, conversion-lag prediction, value-based bidding, uplift modeling, causal inference, experiment design, and audience optimization. Partners with marketing, MarTech, Applied AI, CRM, and data engineering teams to improve channel ROI, build scalable pipelines, and translate ambiguous business questions into measurable solutions.

Designs and operates production GenAI platform infrastructure, including model gateways, routing, caching, evaluation, observability, prompt lifecycle management, guardrails, cost controls, and incident response. Partners across engineering, security, product, data, and applied science while building reliable self-service capabilities for GenAI workloads.

Design and operate Preply’s GenAI platform, including model gateways, routing, caching, evaluation, observability, prompt lifecycle management, guardrails, cost controls, and incident response. Build production backend and ML infrastructure, create developer-facing APIs, establish operational standards, and collaborate with Applied Science, Data, Product, Security, and Engineering teams. Participate in paid on-call rotation and influence platform architecture.

Build and operate production infrastructure for reliable agentic AI systems. Responsibilities include durable agent runtimes, secure tool integration, state and memory management, permissions, guardrails, observability, evaluation, SDKs, workflow frameworks, SLOs, and incident response. The role partners across engineering, security, product, data, and applied science while shaping platform architecture and developer experience.

Build and operate production infrastructure for reliable, secure, stateful AI agents. Responsibilities include durable execution, tool integration, agent state and memory, permissions, guardrails, observability, evaluation, SDKs, and operational standards. The role requires production backend or ML infrastructure experience, strong Python skills, cloud expertise, distributed-systems knowledge, and participation in on-call support.

Build and operate production infrastructure for reliable agentic AI systems, including durable execution, tool integration, state and memory management, permissions, guardrails, observability, evaluation, SDKs, and developer tooling. The role requires production backend or ML infrastructure experience, strong Python skills, cloud expertise, distributed-systems knowledge, and participation in on-call operations.

Designs and operates production GenAI platform infrastructure, including model gateways, routing, caching, evaluation, observability, prompt lifecycle management, guardrails, cost controls, and incident response. Partners with engineering, security, privacy, product, data, and applied science teams to create reliable self-service capabilities for AI workloads.

Own 3–12 month strategic transformation projects spanning retention, monetization, market expansion, partnerships, and M&A evaluation. Lead cross-functional teams, structure ambiguous problems, conduct data-driven analysis and market research, align senior stakeholders, manage workstreams, resolve risks, and deliver measurable business outcomes. Prepare executive and board-level narratives and recommendations while influencing leaders without formal authority.

Own 3–12 month strategic transformation projects spanning retention, monetization, growth, market expansion, partnerships, and M&A evaluation. Lead cross-functional teams, conduct data-driven analysis and market research, align senior stakeholders, manage workstreams, resolve risks, and drive measurable outcomes. Develop executive and board-ready narratives, present recommendations, and connect strategic decisions to commercial and financial results across Preply’s global business.

Build and evaluate AI-powered language-learning solutions by formalizing linguistic phenomena, designing prompts, creating training and evaluation datasets, analyzing model behavior, investigating quality issues, and automating linguistic data processing with Python. Collaborate cross-functionally with engineers, product managers, learning designers, and computational linguists to develop scalable solutions across languages.

Build and evaluate AI-powered language-learning solutions by formalizing linguistic phenomena, designing prompts, creating annotated datasets, analyzing model behavior, and automating evaluation with Python. The role investigates quality issues, validates improvements, and collaborates with engineering, product, and learning-design teams to develop scalable NLP and LLM solutions across languages.

Develops AI-powered language-learning solutions by formalizing linguistic phenomena, designing prompts, building and annotating datasets, evaluating model behavior, investigating quality issues, and automating linguistic data processing with Python. The role collaborates with engineering, product, and learning teams to create scalable NLP solutions across languages.

Lead and grow Preply’s Data Platform engineering team, defining the Lakehouse, ingestion, governance, data quality, and reliability strategy. Partner with Analytics, ML, Data Science, Product, Security, and Engineering leaders to prioritize platform investments, guide architecture, improve operational excellence, and deliver trusted data products. Responsibilities include coaching engineers, hiring, setting SLOs, managing incidents, driving execution, and evolving team structure and technical standards.

Leads the Data Platform engineering organization, setting strategy for the company Lakehouse, ingestion pipelines, data contracts, governance, reliability, and self-service tooling. Manages hiring, team development, execution, architecture, observability, and incident response while partnering with Analytics, ML, Data Science, Product, Security, and Engineering teams. The role balances platform investments with business priorities and drives trusted, scalable data foundations for analytics, machine learning, and AI products.

Leads the Data Platform engineering team and owns its strategy, execution, architecture, reliability, and organizational health. Oversees lakehouse infrastructure, batch and streaming ingestion, data contracts, governance, enrichment, self-service tooling, and platform adoption. Builds and develops high-performing teams, establishes operational standards and SLOs, partners with Analytics, ML, Product, Security, and Engineering, and balances long-term platform investments with near-term business needs.