Preply

Translator

• Localize and review Preply content and brand messaging for our German speaking audience of consumer and business customers
• Be responsible for successful and high-quality execution of all language and translation projects for the German-speaking markets
• Apply transcreation and creative copywriting skills to adapt Preply's marketing and brand content for German-speaking markets, ensuring emotional resonance and cultural authenticity go hand-in-hand with linguistic accuracy
• Maintain and document evolving German content requirements (consistent terminology, brand, tone of voice standards, and compliance)
• Carry out ongoing quality assurance on the German version of the website, blog and email communications to ensure consistency and integrity of translation work and ensure all projects meet our brand guidelines
• Use data analysis and research to identify and deliver improvements to the user experience for the German speaking markets
• Work collaboratively with various internal stakeholders from the marketing, engineering, product and customer insights departments on initiatives for the DACH regions and on general localization initiatives
• Build and maintain close working relationships with a select pool of freelance linguists, acting as their go-to guide on Preply's evolving brand and language standards for German to ensure high-quality, consistent output across all projects
• Support the Quality work-stream activities
• Analyze feedback and scorecards to drive quality improvements
• Triage, investigate and resolve language bugs from stakeholder feedback
• Support the creation of glossaries, style guides, and reference materials
• Ensure consistent updates, version tracking, and brand alignment

Localization Specialist – German

Operations

• Own and run the operational rhythm of the Data & Applied AI org: team syncs, roadmap reviews, OKR cycles, quarterly planning, and retrospectives.
• Lead the annual and quarterly planning processes for the team, linking data and AI initiatives to company-level goals and product strategy.
• Manage the VP's leadership calendar and key team meetings, including agenda setting, preparation of materials, coordination with presenters, and follow-ups for cross-functional reviews, executive updates, and offsites.
• Own the preparation cycle for leadership forums and company-wide reviews where the Data & Applied AI team presents: ensuring the right content, framing, and data are ready.
• Serve as the connective tissue between the Data & Applied AI team and Product, Engineering, Commercial, and other functions, ensuring priorities are aligned and dependencies are visible.
• Own stakeholder communication: regular updates, progress reports, and dashboards that give leadership and partner teams clarity on what the team is delivering and why.
• Represent the Data & Applied AI team in company-wide planning processes (e.g., product planning, OKR alignment, resource allocation).
• Track progress on key data and AI initiatives end-to-end, from scoping through delivery, identifying risks early and driving resolution.
• Support the VP in managing the team's portfolio of work: ensuring the right things are prioritised, resourced, and moving forward.
• Drive accountability across workstreams without being a bottleneck, using lightweight but effective tracking and reporting mechanisms.
• Continuously identify friction in how the team works, whether in processes, tooling, collaboration patterns, or information flow, and design practical improvements.
• Build and maintain scalable processes that help the team operate efficiently as it grows (e.g., intake workflows, documentation standards, knowledge sharing).
• Support headcount planning, resource allocation, and team capacity analysis with data-driven models.
• Lead or support high-impact strategic projects that span the Data & Applied AI org, such as evolving the team's operating model, defining AI adoption frameworks, or assessing build-vs-buy decisions for data infrastructure.
• Conduct analysis and research to inform key decisions: sizing opportunities, benchmarking approaches, or evaluating trade-offs.

Senior Data Strategy – Operations Lead

Machine Learning Engineer

• Build and maintain ML pipelines for training, evaluation, and deployment using tools like Databricks, MLFlow, Airflow, DBT, Sagemaker, Tecton
• Support AI scientist creating reproducible, containerized model training environments (on-demand and scheduled), and manage compute at scale (e.g., spot/GPU autoscaling)
• Define and implement observability and alerting for ML systems (model drift, data quality, feature coverage, etc.)
• Design and scale data ingestion and feature transformation flows using batch (e.g., Spark/BigQuery) and streaming (Kafka or equivalent)
• Contribute to internal Python libraries and platform tooling that accelerate experimentation and deployment for all model teams
• Ensure ML services are modular, testable, and monitored from day one
• Exploration and productionization of LLM-based features (e.g., retrieval pipelines, prompt evaluation, model serving)

Senior Machine Learning Platform/Ops Engineer

Data Analyst

• Create analytical frameworks to track performance, extract insights, and guide strategic decisions.
• Design and maintain experimentation playbooks to standardize testing across SEO/LLMs, content and DPR functions
• Build impact estimation models to evaluate ideas, support prioritization, and justify resourcing with solid business cases.
• Act as the primary SEO-Data team liaison, translating SEO needs into technical requirements for dashboards, tracking, and APIs.
• Collaborate with the Head of SEO to define OKRs, reporting structures, and experimentation cadences across the inbound team.
• Help shape the team’s testing backlog and learning roadmap, ensuring alignment with company strategy and impact goals.
• Work hands-on with tools like GSC, Ahrefs, Looker, Amplitude, and internal analytics platforms to extract and communicate insights.
• Build and scale internal templates for prioritization scoring, test evaluation, and impact reporting.
• Use AI tools to automate and optimize analysis workflows for speed, quality, and clarity across the team.
• Synthesize and present complex analyses and test results to senior managers and directors with clarity and business relevance.

SEO and LLM Analytics Manager

Senior Data Strategy, Operations Lead

Security Engineer

• Own application and product security, partnering closely with engineering teams to improve security outcomes across the full SDLC
• Act as a strong technical voice in how we design, build, ship, and operate secure systems, driving initiatives end-to-end through influence, collaboration, and hands-on execution
• Work hands-on with our core backend stack (Python, Django), reading and writing code, contributing improvements, and building automation to scale security with product engineering teams to embed security into planning, design, and delivery, without slowing teams down
• Participate in architecture discussions and design reviews to identify risk early and propose pragmatic mitigations
• Lead and facilitate threat modeling for new features and significant changes, and translate results into prioritized engineering work
• Improve the secure SDLC end-to-end: requirements, secure design, implementation guidance, testing, release, and operational readiness
• Build “paved paths” and guardrails that make secure choices the default (libraries, patterns, templates, CI checks)
• Mature code and application security tooling, including selection, rollout, and adoption: SAST, SCA (We now use Snyk), secret scanning, and relevant DAST/API testing where it adds signal
• Integrate findings into developer workflows with clear ownership, SLAs, and low-friction remediation
• Proactively discover security issues through code review support, automation, security testing, and targeted assessments
• Improve vulnerability management for application and product security findings: triage, prioritization, remediation, verification, and trend reporting
• Create and deliver training and enablement for engineers (secure coding, common pitfalls, new patterns), and help grow security champions across teams
• Partner with GRC to ensure security requirements and controls are feasible, well understood, and evidenced through real engineering practice
• Lead engineering wide initiatives, managing stakeholders and aligning with business to deliver high impact results

Senior Security Engineer – Application

• Own platform security across AWS, Kubernetes, and GCP, with a strong focus on detection, alerting, and incident response readiness
• Lead key platform security initiatives end-to-end, from problem definition through rollout and iteration
• Act as a strong technical voice in defining how platform security is designed, implemented, and operated at Preply
• Evolve our monitoring from “we have a SIEM” to a detection and response capability that is effective, trusted, and actionable
• Work hands-on with our current tooling, including Datadog as our SIEM, Okta as our primary IdP, and a range of SaaS platforms
• Drive cross-functional platform security initiatives from problem definition to production rollout, partnering with SRE, Data, Engineering, and GRC
• Strengthen cloud and platform security across AWS and Kubernetes, with expanding scope in GCP, through practical guardrails, secure patterns, and automation
• Improve the robustness of access to internal infrastructure, including identity, privileged access, and auditability
• Improve security of Kubernetes deployments, including cluster and workload security, policy enforcement, and secure workload identity patterns
• Mature detection and response capability using Datadog
• Improve log coverage and data quality (cloud, Kubernetes, CI/CD, identity, and key SaaS)
• Build and tune actionable detections with clear severity, ownership, and expected frequency
• Reduce noise through correlation, deduplication, enrichment, and continuous tuning
• Establish repeatable triage workflows and clear escalation paths, being part of the Security on-call rotations
• Create investigation playbooks and runbooks so alerts can be handled consistently and quickly
• Partner with Data teams to improve monitoring for suspicious activity and sensitive access patterns, with an emphasis on practical, high-signal alerting
• Improve secrets management and reduce exposure risk across CI/CD and runtime
• Build security automation that makes the secure path the easy path for engineers

Localization Specialist – German

Senior Data Strategy – Operations Lead

Senior Machine Learning Platform/Ops Engineer

SEO and LLM Analytics Manager

Senior Machine Learning Platform/Ops Engineer

Senior Machine Learning Platform/Ops Engineer

Senior Data Strategy, Operations Lead

Senior Security Engineer – Application

Senior Security Engineer – Platform