Вакансии

Location: Москва Мы команда исследователей и инженеров, разрабатывающих технологии post-training (SFT, RL) и контролируемой генерации для моделей генерации изображений, видео и омни-модальных. Мы сфокусированы на повышение качества, управляемости и устойчивости генеративных моделей с помощью методов RLHF / RLAIF и инструктивного редактирования. Помимо фундаментальных исследований, мы создаём прикладные решения для B2B-партнёров от системы генерации интерьера до системы генерации персонализированных аватаров. Обязанности * проектирование и обучение высокопроизводительных диффузионных моделей (R2V, R2I, R2V+A) для редактирования и генерации видео / изображений * разработка новых подходов и архитектурных решений для post-training фазы диффузионных моделей: RLHF (PPO, DPO и др.), RLAIF, SFT * разработка, масштабирование и поддержание RL пайплайнов * руководство экспериментами: постановка гипотез, разработка протоколов, анализ результатов * проведение code review, менторинг младших исследователей и инженеров, участие в планировании исследовательской дорожной карты * взаимодействие с командами Pretraining, Data, Infrastructure, Production для масштаб пайплайнов и повышения стабильности моделей в продакшене * поддержание и внедрение SOTA-подходов: мониторинг литературы (arxiv, NeurIPS, ICML, CVPR, ICLR), инициирование внутренних исследований. Требования * бакалавриат/магистратура по направлению компьютерных наук/прикладной математике/ машинному обучению или близких к ним * 3+ лет релевантного опыта исследований и разработки в области глубокого обучения/компьютерного зрения/генеративного ИИ * глубокие знания в Computer Vision и Generative Modeling: Diffusion Models, GANs, VAEs, Flow/Rectified Flow Matching * опыт работы с современными диффузионными фреймворками (Diffusers) и моделями FLUX, Wan 2.X и др.) * уверенное владение PyTorch и навыки распределённого обучения (DDP/FSDP) * понимание и практическое применение RL и RLHF ( PPO/DPO и др.) * умение проектировать архитектуры, планировать эксперименты и интерпретировать результаты. Будет плюсом: * наличие PhD * опыт публикаций или активное участие в исследованиях уровня NeurIPS / ICLR/ CVPR. Условия * конкурентный уровень заработной платы, годовые премии по результатам работы * участие в развитии и создании OpenSource продуктов * корпоративная пенсионная программа, страхование от несчастных случаев, социальные гарантии, ДМС * комфортный офис Sbergile Home (метро Кутузовская).

Location: Worldwide; Georgia; Armenia; Serbia; Poland Social Discovery Group (SDG) is a group of social discovery companies. SDG solves the problems of loneliness, isolation, and disconnection - transforming virtual intimacy into the new normal. SDG’s products redefine the way people interact and connect with one another. Our portfolio includes social entertainment platforms designed to connect people online across different cultures and regions of the world. We bring together a team of like-minded people and IT professionals who specialize in creating and developing globally impactful social discovery products. Our international team of digital nomads works remotely from all over the world. We’re proud to be a two-time “Great Place to Work” winner (USA & Japan, 2024–2025) and a Top-5 Company for Work-From-Anywhere Jobs (FlexJobs, 2025). We are looking for a Team Lead Software Engineer (.NET) . Your main tasks will be: Own delivery of the monolith-to-microservices modernization end-to-end — from legacy analysis through production release — with full visibility into pipeline status and risk. Partner with Product and Architecture to review specs, map legacy dependencies, and turn that into estimates the team can actually hit. Guide architectural decisions across .NET Core services, databases (SQL/NoSQL), and frontend integrations, and be the go-to for the hardest backend, data migration, and production incident calls. Sharpen how the team works day-to-day — tighter task tracking in Jira, more accurate estimation, and a QA partnership that keeps bugs resolved fast and production stable. Run regular 1-on-1s tied to real career goals, and drive the feedback and process changes that keep the team improving and engaged. We expect from you: Solid .NET engineering background with deep expertise in C#, .NET 8, and ASP.NET Core. Deep understanding of web application architecture, REST APIs, the microservices pattern, message brokers (RabbitMQ), and database design (PostgreSQL, MS SQL Server, Redis). Practical experience with legacy system modernization, monorepo-to-microservices refactoring, and containerization (Docker, Kubernetes). Ability to act as the technical escalation point for complex implementation, data migration, or performance issues. Proven experience managing engineering teams — running 1-on-1s, performance reviews, and objective evaluations. Strong track record in delivery management: task tracking in Jira, process discipline, and hitting internal deadlines with predictable releases. Experience reviewing business specs, assessing technical feasibility, and providing high-level estimates — partnering closely with PM and product to keep engineering output aligned with product goals. What do we offer: REMOTE OPPORTUNITY to work full-time; The initial pay level or pay range for this role will be shared with candidates during the recruitment process and before the commencement of employment; Vacation 28 calendar days per year; 7 wellness days per year (time off) that can be used to deal with household issues, to lie down and recover without taking sick leave; Bonuses up to $5000 for recommending successful applicants for positions in the company; 50% payment for professional training, international conferences, and meetings; Corporate discount for English lessons; ​ Health benefits. According to the paychecks, if you are not eligible for corporate medical insurance, the company will compensate you with up to $ 1,000 gross per year per employee. This can be spent on self-purchase of health insurance or on doctor’s fees for yourself and close relatives (spouse, children); ​ Workplace organization. The company provides all employees with an equipped workplace and all the necessary equipment (table, armchair, wifi, etc.) in our offices or co-working locations. In the other locations, the company provides reimbursement of workplace costs up to $ 1000 gross once every 3 years, according to the paychecks. This money can be spent on the rent of the co-working room, on equipping the working place at home (desk, chair, Internet, etc.) during those 3 years; Internal gamified gratitude system: receive bonuses from colleagues and exchange them for our merchandise, team building activities, massage certificates, etc. Sounds good? Join us now! The initial pay level or pay range for this role will be shared with candidates during the recruitment process and before the commencement of employment.

Workplace: fully Role Overview: Join a manufacturing sector project focused on migrating ERP systems to Microsoft Dynamics 365 and building a modern analytics platform using Microsoft Fabric and Power BI . The role combines business-focused Sales & Operations Planning (S&OP) initiatives with analytics and data modeling responsibilities, helping transform planning and operational data into reliable business insights. You will work across D365 , Microsoft Fabric , Power BI , Lakehouse architecture , and modern data analytics practices to support planning, reporting, and decision-making processes. Key Responsibilities: Help shape the client's Sales & Operations Planning (S&OP) process, aligning sales, demand, supply, and inventory planning across a manufacturing business. Collaborate with business stakeholders to assess current planning practices, identify gaps, and define planning requirements, KPIs, and reporting priorities. Translate S&OP requirements into analytical data models and Power BI dashboards supporting demand and supply balancing, inventory visibility, and plan-versus-actual analysis. Identify and integrate data from Dynamics 365 and other business systems into Microsoft Fabric , ensuring reliable and consistent planning inputs. Collaborate with the ERP implementation team to align analytics solutions with business processes and data structures introduced through the Dynamics 365 migration. Validate data and analytical outputs with business users, investigate discrepancies, and support solution adoption during regular S&OP reviews. Design and develop analytics solutions using Microsoft Fabric and Power BI . Develop and optimize data processing solutions using Spark and SQL . Contribute to Lakehouse and Data Warehouse (DWH) architecture implementation, including Medallion Architecture (Bronze → Silver → Gold) . Must-Have Requirements: Hands-on experience implementing or improving Sales & Operations Planning (S&OP) processes in a manufacturing environment, including demand and supply planning, inventory management, and alignment between sales and operations. Hands-on experience with Microsoft Fabric . Experience in D365 data analysis and data modeling. Strong knowledge of Spark and SQL . Understanding of Lakehouse , DWH concepts , and data modeling . Experience working with Medallion Architecture (Bronze → Silver → Gold) . 👍 English: upper-intermediate 🌍 Location: 📩 Ready to Join? We look forward to receiving your application and welcoming you to our team!

Location: Remote, Global ABOUT SUPABASE Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. More than 9 million developers and 250,000+ customers now build on Supabase, and the majority of new databases on the platform are created by AI app builders — so the systems underneath have to stay reliable while they grow fast. ABOUT THE ROLE Container Engineering (ConEng) owns the internal container platform that runs Supabase's multi-tenant, multi-region services — the managed Kubernetes platform that other engineering teams build and deploy on top of. We own cluster lifecycle and cluster classes, multi-cluster and multi-region topology, capacity, scheduling, isolation and runtime safety, the higher-level platform APIs, the Kubernetes deployment contract and Argo semantics, rollout primitives, and runtime configuration and secret injection.   Your job is to make the platform itself trustworthy, observable, and cheap to operate at scale — and to raise the reliability bar for everyone who depends on it. This is not a gatekeeper role. You'll make the reliable path the easy path: building the automation and platform APIs that let product teams run safely by default, instrumenting what we operate, and making sure that when something breaks we detect it and recover fast.   The work spans three strength areas: Tooling & ecosystem — deep knowledge of the container and orchestration landscape (Kubernetes, containerd/OCI, the CNCF ecosystem) and a clear point of view on how to compose it. Software engineering — designing and building the platform's automation, controllers, and internal tooling as production code, including solutions that don't come pre-shaped by an existing tool. Operational / SRE practice — the discipline (SLOs, error budgets, incident response, ORRs) that runs the platform at scale as a predictable one. We expect you to be genuinely deep in at least one of these, and many of our strongest candidates are deep in two (usually leaning toward one). Bringing real strength across all three is rare and exceptional — and even then, no one is equally strong in all three at once, so you'd focus where you're most needed at any given time. Different profiles are welcome here; tell us which of these is your center of gravity. WHAT YOU'LL BE RESPONSIBLE FOR Regardless of the skill set, all team members carry a share of the team's on-call rota, responding to platform incidents and driving them to resolution and follow-up. Platform reliability & operations - Define SLIs/SLOs and error budgets for the container platform itself, and build error-budget policies that actually gate platform changes. - Own the Operational Readiness Review (ORR) process for new platform capabilities and major changes. - Design and track metrics for platform uptime, and increase observability into the platform by capturing the right metrics, logs, and traces across the platform layers. - Drive the incident-to-improvement pipeline: facilitate blameless postmortems, connect findings to readiness gaps, spot repeat failure patterns, and push through systemic fixes. Software engineering - Build and maintain the platform's automation, controllers, operators, and internal tooling — writing production code and shipping it, not just wiring together existing tools. - Design and implement the platform-side CI/CD and rollout primitives that speed up platform deployments while working within proper change and release management. - Implement and maintain security automation for the platform: intrusion detection, automated remediation, and patch management. - Identify and quantify operational toil, and build the automation that removes it rather than just documenting it. - Contribute to the platform's continual testing automation — the test suites and pipelines that validate platform releases are fit for purpose before they reach tenants. Tooling, ecosystem & architecture - Act as a platform reliability and architecture expert — contribute to failure-mode analysis, dependency mapping, and resilience design for multi-tenant container workloads. - Evaluate and evolve the platform's tooling ecosystem (orchestration, runtime, networking, isolation, and observability stack) with a clear, opinionated view on trade-offs. - Improve system architecture to reduce infrastructure cost while balancing security, reliability, and performance across tenants. - Guide teams building on the platform to design sustainable on-call practices for their own services: alert quality, escalation paths, runbook coverage, and noise reduction.   YOU MIGHT BE A GOOD FIT IF YOU - Have 5+ years in platform engineering, container/Kubernetes platform operations, SRE, or production infrastructure, and have operated production systems at scale and carried on-call for them. - Are genuinely deep in at least one — ideally two — of the three strength areas below (and tell us which is your center of gravity): - Tooling & ecosystem — know the container/orchestration landscape well: Kubernetes, containerd and the OCI/CRI standards, and the surrounding CNCF ecosystem (e.g. Argo CD/Argo Rollouts for GitOps and progressive delivery; Cilium/eBPF networking and the Gateway API; Karpenter/KEDA-style autoscaling; and workload-isolation runtimes such as gVisor or Kata Containers). You reason about trade-offs rather than reaching for defaults. - Software engineering — write real production code (Go and/or a comparable systems language) and can build controllers, operators, and platform APIs — stepping back to the underlying problem and building the right solution when no off-the-shelf tool fits. - Operational / SRE practice — are fluent in SLOs, error budgets, and operational KPIs; have led incident response and blameless postmortems; and have driven down MTTD/MTTR for systems you operated. - Have worked with large-scale multi-tenant systems and understand isolation as a spectrum — from namespaces, RBAC, network policies, and resource quotas through to stronger primitives such as virtual clusters (e.g. vCluster) or VM-class runtimes. - Operate confidently on AWS (multiple accounts, IAM, VPC) and are comfortable with infrastructure-as-code (Pulumi preferred; Terraform/CDK also fine) and Kubernetes. - Script and automate to eliminate toil rather than absorb it. - Communicate clearly with both infrastructure specialists and product engineers, and thrive in async, globally distributed teams. - Are comfortable navigating ambiguity and iterating toward better systems over time. Nice to Have - Experience building and operating a multi-tenant Kubernetes platform that internal teams deploy onto. - Familiarity with policy engines (Kyverno, or OPA/Gatekeeper) and cost-attribution tooling (OpenCost/Kubecost) for per-tenant chargeback/showback. - Experience with managed database platforms or Postgres. - Familiarity with OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, or similar observability tooling. WHAT WE OFFER - Fully Remote We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world. - ESOP Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together. - Tech Allowance Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work. - Health Benefits Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us. - Annual Off-Sites Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year. - Flexible Work We operate asynchronously and trust you to manage your own time. You know what needs to be done and when. - Professional Development Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth. ABOUT THE TEAM Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love. - ~400 team members - 60+ countries - 20+ languages spoken - Over $1B raised (including our $500M Series F) - 540,000+ community members We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.

Тебе предстоит Повысить эффективность пайплайнов для расчета с daily-признаков на основе batch процессинга. Создать эффективный пайплайн для расчёта realtime-признаков на основе Kafka‑топиков.

Location: Москва Команда занимается разработкой «AI-помощника» - это проект с использованием LLM, который призван радикально улучшить клиентский опыт при при оформлении основных продуктов банка и помочь клиенту разобраться в продуктах. Обязанности * проведение исследовательского анализа данных (EDA) для выявления ключевых метрик и закономерностей, построение моделей. Data-driven подход к разработке * разработка и внедрение AI-агентов на базе LLM (SOTA open-source решения), в т.ч участие в создании и развитии RAG-систем (Retrieval-Augmented Generation) * работа с LLM: написание и оптимизация промптов (prompt engineering), управление взаимодействием с моделью * интеграция решений с корпоративными системами, обеспечение их взаимодействия * создание автономных бизнес-процессов в компании. Требования * опыт работы с Python и библиотеками: pytorch, pandas, numpy, SQLAlchemy, Scikit-learn, Matplotlib/Seaborn * писать поддерживаемый, читаемый код; участвовать в ревью, обсуждениях и формировании технических подходов * практический опыт в NLP: векторизация текста, классификация запросов, построение RAG-систем, дообучение языковых моделей, эмбеддингов и пр. * понимание классических алгоритмов ML * онимание методов обучения нейросетевых моделей (transfer learning, fine tuning) * навыки работы с Git, опыт интеграции решений в промышленные среды. Условия * офисный формат работы - офис на Кутузовской, сзонами отдыха и спортзалом; * годовой бонус и ежегодный пересмотр * социальный пакет (ДМС, страхование от несчастных случаев, льготное кредитование, подключение родственников к полису) * корпоративный университет Сбера, внутренняя образовательная платформа, участие в IT-конференциях * льготная ипотека в Сбере, корпоративная пенсионная программа, подписка СберПрайм+ с возможностью использования до трех близких, скидки от партнеров * материальная помощь при наступлении значимых событий в жизни; * дисконт-программы от компаний партнеров (фитнес, страхование, туризм) * подарки и мероприятия для детей сотрудников. Оздоровительные программы для детей сотрудников.

Location: Bengaluru Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW Harvey is redefining what’s possible in legal work by combining cutting-edge AI with intuitive product experiences. As we scale rapidly and take on increasingly complex use cases, we’re expanding our product engineering team to accelerate our ability to ship fast, delight users, and build infrastructure that scales with us. As a Senior Engineering Manager for Product Engineering, you’ll lead teams of backend engineers building the core product surfaces that lawyers and legal professionals use every day—from complex workflows and collaboration tools to elegant user interfaces powered by sophisticated backend systems. This role is equal parts product intuition and technical execution. You’ll be trusted to make fast, high-quality decisions, ship impactful features, and grow a team that embodies engineering excellence. We move with urgency, bias toward simplicity, and expect our leaders to be hands-on when needed. If you’re energized by ownership, decisive execution, and solving real user problems, we'd love to hear from you. WHAT YOU'LL DO - Lead engineers define, build, and drive the multi-quarter technical strategy for user-facing features across our product. - Collaborate closely with Design, Product, and AI teams to identify and execute on high-impact initiatives. - Help define and evolve the architecture for a fast-moving, AI-powered application. - Hire, grow, and retain exceptional engineers who thrive in a high-trust, high-impact environment. - Foster a culture of simplicity, quality, and continuous improvement. - Contribute to code and architecture reviews, unblock teams, and lead by example when needed. - Work closely with cross-functional partners to align product initiatives with company goals. WHAT YOU HAVE - 3+ years of experience in a software engineering management role leading product engineering teams, preferably in high-growth environments. - 6+ years of software engineering experience as a backend engineer. - A track record of leading high-performing, engaged engineering teams. - Strong product sense and an instinct for balancing speed, quality, and user impact. - Experience with backend systems (e.g., Python, Java, Go) and modern cloud-native infrastructure. - A plus: Exposure to AI-powered applications or complex internal tools. - Ability to anticipate scaling bottlenecks before they occur and take decisive, proactive measures. - Experience building RAG pipelines and managing the data lifecycle. ADDITIONAL INFORMATION - Work eligibility: Must be authorized to work in India. Visa sponsorship is not available for this role. PLEASE FIND THE APPLICANT PRIVACY FOR THIS REGION HERE https://www.harvey.ai/legal/india-applicant-privacy-notice. #LI-DM1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

• Проектировать и создавать ИИ-инструменты для автоматического производства дизайн-макетов.

TLDR: Ищем Applied AI Engineer (Middle+) для разработки и тестирования AI-систем, улучшения качества и автоматизации внутренних процессов. - 3+ года опыта в разработке AI/LLM-систем.

Location: Санкт-Петербург Команда разработала и внедрила большое количество ML моделей в процессы банка, наши модели улучшают клиенсткий опыт, помогают снизить отток и формируют персональные рекомендаций для клиентов. С помощью стека разработанных моделей, команда улучшает внутренние процессы банка, а так же платные продукты и сервисы для клиента. Мы ищем в команду ML инженера, который поможет лидировать направление разработки ML моделей в команде, улучшать стек текущих моделей и разрабатывать новые. Обязанности * погрузиться в процессы и продукты компании, с целью улучшения процессов с помощью ML * разрабатывать и тестировать новые гипотезы связанные с ML моделями на табличных данных * разрабатывать MVP решения с использованием LLM моделей (промпт-инжиниринг) * разрабатывать ML модели с учетом требований банка и последующего вывода их в ПРОД * готовить аналитику результатам работы моделей и презентовать ее заказчикам * участвовать в развитии ML инструментов и тех. стека Банка. Требования * опыт работы по направлению Data Science от 3х лет * знание Python ML стека (pandas / sklearn / catboost ) * опыт использования LLM * умение переложить гипотезу с бизнес терминов на техническую реализацию * развитые коммуникативные способности для эффективного общения * знание SQL / Git / Pep8. Будет плюсом опыт:  * промышленной разработки на Python от 1 года * внедрения ML решений в бизнес процессы * работы с BigData стеком (Hadoop / PySpark) * работы с Linux, bash. Условия • удобный офис в СПб на ул. Уральской, дом 1 (шаттлы от метро Спортивная и Приморская) • офисный формат работы • регулярный пересмотр заработной платы и возможность получения годовой премии • доступ к более чем 400 образовательным программам СберУниверситета • программу адаптации и поддержку наставника на первых этапах работы • расширенную медицинскую страховку (ДМС) и выгодные условия страхования для членов семьи • ипотечные программы для сотрудников • подписка Прайм с возможностью совместного использования на трёх близких • вознаграждения за рекомендации кандидатов.

Location: San Francisco Perplexity is seeking energetic engineers to join our highly driven Agent Engine team. The Agent Engine team designs and builds powerful agentic experiences including Perplexity Computer https://www.perplexity.ai/computer, the Comet ecosystem, our Agent API, and more. In short, we build frontier intelligence systems for the world's most discerning users. As an engineer on our Agent Engine team, you'll bring AI expertise, sharp product intuition, and a tinkerer's mindset to advance the frontier of what agents can accomplish for our millions of devoted users. You'll work across engineering and applied research to solve many open problems in AI, including: - Designing frontier agent harnesses and systems to perform high-stakes, mission-critical work for our users; - Leveraging action and decision models that determine, based on complex multimodal states, how to accomplish user-specified objectives; - Providing consistently excellent experiences across local execution, headless cloud, custom executors, and other environments through sound system design and flexible abstractions; - Developing permission architectures, payload classifiers, and other methods to implement secure-by-design agentic capabilities; - Designing optimal data representations and modes of interaction between agents and their environments; - and much, much more. QUALIFICATIONS - Strong foundational familiarity with the AI systems stack. - Proficiency in Python (bonus points for TypeScript, Go, and/or Rust). - Significant experience in at least one of the following areas: - Harness engineering - Agent Skills and plugin architectures - Model training and reinforcement learning - Browser technologies (CDP, Playwright, extension development, etc.) - Strong technical intuition and taste for user experience excellence. - Comfortable working with a small, fast-moving team; bias toward ownership. - A passion for shipping products that surprise and delight.

Location: AU: Melbourne: (260 Burwood Rd); AU: Queensland Remote Worker; AU: New South Wales Remote Worker; AU: Sydney (45 Clarence St); AU: Brisbane (243 Edward St); AU: Victoria Remote Worker Workplace: Hybrid SENIOR ENGINEER - PLATFORM The role and its impact As a Senior Engineer in our Infrastructure team, you’ll help Xero engineers build and test software faster and with more confidence. You’ll design and build the systems that create test environments, and you’ll make sure test data is ready when teams need it. This work sits at the heart of how we ship software, and it helps set Xero up for AI transformation. You’ll work across complex platforms, including older legacy systems alongside modern technology. There will be trade-offs to weigh as you decide where the most value sits for engineering teams. Xero asks you to Go Bold in how you approach these problems, and to Go Fast without cutting corners. The team and how they connect Our Infrastructure team builds the platform foundations that Xero engineers rely on every day. Our engineers come from software, platform and infrastructure backgrounds, and we work closely with teams across Xero to understand their systems and find elegant solutions. We believe in Going Together, which means sharing knowledge openly and making sure everyone’s voice is heard. The team is currently working on / Initially, you will focus on: - Building support systems and tooling for test environments, so engineers can test changes without waiting on blockers. - Improving test data and enabling frictionless testing across engineering teams. - Working with our Golang services, alongside infrastructure built with Terraform on AWS, including Kubernetes, EC2 and Lambda. - Contributing to a longer-running initiative to shorten feedback loops and increase engineering productivity. Where and how you can work This is a permanent role, based in Melbourne, Sydney or Brisbane with a remote-first working culture. We have no set office days, so you’ll work with your manager and team to find a balance that suits how you do your best work. Once you’ve settled in, you’ll join our on-call rotation as part of supporting the systems we build. Here are some of the things we’re looking for, for this role: - Your background could be in software engineering that has moved into platform or DevOps, or in infrastructure work that understands how software is built. - Golang is our preferred language, though strong experience in Java or .NET is equally welcome. - Hands-on experience with Terraform, AWS and Kubernetes will help you hit the ground running. - A curious, problem-solving mindset and the ability to pick up unfamiliar systems quickly, including legacy ones, are key to how you’ll Go Further here. - Clear communication is second nature to you, whether you’re working in another team’s codebase or presenting to leadership. Apply even if your experience isn’t a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Location: Singapore Why This Role Exists Cloudflare built its reputation helping build a better Internet, defending millions of sites, giving away SSL and DDoS mitigation when the industry charged premium prices. In an acceleratingly dangerous world, the scope of that mission has changed. We are becoming something more: critical infrastructure. Banks run their payment rails on us. Governments run public services on us. Media companies depend on us during live events. Health systems depend on us to provide care. Reliability for these customers is no longer a feature of our product. It is a mission. Serving that customer base demands a different operating model. Traditional support organizations route tickets. Traditional engineering organizations ship features. Neither alone is enough when the stakes are this high. We are pivoting to something different: a customer-facing engineering organization, directly engaged with our customers at scale. This is work a central dev team cannot do from the inside of the network. The Customer Reliability Engineering function is the spine of that pivot. CRE is SRE applied outward, the same engineering discipline, applied to the reliability of the systems our customers run on Cloudflare. You are the engineer who owns the problems that matter most to the customers who matter most, and you contribute directly to our products and tooling, in partnership with Product Engineering, to hold that standard across the entire customer base. The Role CRE is a rapid response team and a proactive engineering team. You fix things at the edge as they come up, and you help build the product capabilities that identify customer issues before they become a crisis. Both modes are equally core. Rapid response. When a customer issue surfaces that is high-severity, cross-layer and complex, you are the engineer who answers. You reproduce the defect, isolate the root cause across Cloudflare's infrastructure and the customer's stack, drive the fix with Product Engineering, and confirm resolution. You hold on-call for high-severity incidents as part of a global shift rotation. Proactive engineering. When no fire is burning, you work with Product Engineering and our platform teams to build the capabilities that make the next fire cheaper or unnecessary: telemetry pipelines that correlate signals across the customer base, detectors that fire before a human notices, diagnostic tooling that scales across hundreds of customers, automation that reduces toil for Customer Support. Every incident you carry generates engineering output that reduces the cost of recurrence. The work compounds. Cloudflare is building CRE as an AI-native function. You will work with and help build agents and tooling that pre-diagnose incidents, surface relevant logs and configuration, and propose fixes with cited evidence. Engineers who ship AI-assisted diagnostics are the ones defining this discipline. What You Might Work On Rapid response: Own a Sev-1 incident where a large financial services customer sees asymmetric latency from a single POP. Trace it through BGP routing and origin configuration. Produce the fix upstream. Diagnose a recurring WebSocket disconnect that a media customer has been fighting for weeks. Isolate it to a specific interaction between WAF and their origin load balancer. Drive the fix with Product Engineering. Partner with a government customer's SRE team during an active DDoS event. Help them shape their Magic Transit and WAF configuration in real time. Proactive engineering: Build, with Product Engineering, a distributed tracing capability that correlates Cloudflare edge signals with customer origin metrics so a single query tells the story of a failing request end-to-end. Ship a detector for a class of WAF false positives silently degrading several customers. Get it into production before the next renewal cycle. Prototype an AI agent that takes a new customer case, pulls relevant logs and config, and proposes a root cause with linked evidence. Deploy it internally. Measure whether it makes engineers faster. Iterate. Responsibilities Rapid incident response and root cause analysis. Own the most complex, high-severity customer issues end-to-end, from first signal through confirmed resolution. Lead deep-dive debugging across the full stack: edge, network, DNS, transport, APIs, application, customer-side configuration. Reproduce defects, validate fixes with Engineering, and confirm customer-side resolution. Produce postmortems other engineers rely on. Hold on-call for high-severity incidents as part of a global rotation that includes weekends. Proactive reliability engineering. Analyze support and telemetry signals across the customer base to find systemic risks before they become incidents. Contribute monitoring, detection, and diagnostic capability to the core product and the engineering systems that give Customer Support early visibility into customer-affecting issues. Define customer-facing reliability metrics (error rates, resolution times, repeat-contact rates) and drive measurable improvement. Write automation that reduces mean-time-to-detect and mean-time-to-resolve. Cross-functional partnership. Manage the technical escalation lifecycle with clear ownership and timely communication. Partner with Product Engineering to drive fixes, workarounds, and configuration changes that address underlying gaps. Represent the customer reliability perspective in engineering syncs, incident reviews, and post-mortem processes. Technical leadership and enablement. Raise the technical floor of Customer Support through pair-debugging, structured knowledge transfer, and shared tooling. Document diagnostic procedures and resolution patterns in runbooks, internal knowledge bases, and AI skills. Share insights from customer-facing incidents to improve product documentation and operational readiness. Product and platform depth. Maintain deep, current expertise across Cloudflare's product portfolio: edge networking, DNS, CDN, WAF, DDoS mitigation, Zero Trust, Workers, and our developer platform. Anticipate customer impact from new releases and architecture changes. Serve as a go-to subject-matter expert in one or more domains. Requirements Minimum 5 years of hands-on experience in site reliability engineering, escalation engineering, systems engineering, or a comparable deeply technical support / operations role, with at least 2 years in customer-facing environments. Strong foundation in networking and security: TCP/IP fundamentals: OSI model, IPv4/IPv6 addressing, subnetting, routing, switching. Core protocols: DNS, HTTP/S, TLS/SSL, SMTP, SNMP, NTP. Routing protocols: BGP, OSPF, including path selection and route propagation. Firewall concepts: stateful/stateless inspection, rule sets, NAT, ACLs. VPN and encryption: IPSec, SSL/TLS tunnels, GRE. Zero Trust architecture, network segmentation, modern security models. Proficiency with observability and diagnostic tooling: packet capture and analysis (Wireshark, tcpdump), log aggregation (Kibana, Elasticsearch), metrics dashboards (Grafana), distributed tracing. Strong scripting and automation skills (Bash, Python) with a track record of shipping tooling that improves reliability and reduces toil. Experience with incident management, postmortem culture, and SLO/SLI-based reliability practices. Excellent written and verbal communication. Able to convey complex technical information clearly to engineers, leadership, and customers. Comfort owning ambiguous, cross-layer problems. Composure under pressure during high-severity incidents. Desired Skills & Experience SRE, DevOps, or platform engineering experience with direct customer-facing accountability. Deep expertise at both L3/L4 (network infrastructure) and L7 (application protocols, DNS, HTTP, WebSocket). Expert-level proficiency with Linux command-line tools: curl, dig, git, traceroute, mtr, strace, ss. Data-at-scale analysis using SQL, PromQL, or equivalent. Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform, Pulumi), and container orchestration (Kubernetes, Docker). Track record of building internal tooling or diagnostic utilities that measurably improved team efficiency. Demonstrated technical leadership: mentoring engineers, driving cross-team initiatives, influencing outcomes without direct authority. Experience applying AI/ML to production engineering or operational workflows. Comfort engaging directly with enterprise customer engineering teams, including on calls during incidents. Bonus Points Active Cloudflare user who understands the platform as a practitioner. Hands-on experience with Workers, Pages, R2, D1, or other developer platform services. Cloud networking and security experience across AWS, Azure, or GCP. Web programming (HTML, JavaScript) and regular expressions. Chaos engineering or formal reliability frameworks (e.g., Google SRE principles). Managing or configuring non-HTTP services: email, DNS authoritative/recursive, FTP, SSH. What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Athens, Greece We are looking for a highly skilled Senior Data Platform Engineer to join our Data Platform team and play a key role in designing, building, and evolving the company’s data infrastructure. This is a hands-on engineering position focused on developing scalable data platforms, high-volume ingestion pipelines, and the foundational data systems that power analytics, product applications, AI workloads, and large-scale model execution. You will help architect our data lake, build reliable ingestion capabilities at scale, and shape the core infrastructure that supports the company’s future growth. This is an exciting opportunity to work with cutting-edge technologies and have a defining impact on the architecture and evolution of our data platform. Requirements: 5+ years of experience as a Data Platform/Infra Engineer Proven track record of building and operating scalable data infrastructure specifically within Data Lake architectures. Proven experience designing and operating large-scale data processing workloads on Kubernetes, including workload autoscaling with KEDA. Experience with Snowflake. Strong proficiency in Python and SQL. Hands-on experience with Infrastructure as Code, preferably Terraform. Experience with managing a data orchestration platform such as Airflow or Prefect Responsibilities: Build complex, high-volume batch and near-real-time ingestion services while helping evolve the platform’s streaming capabilities. Design and implement data solutions for all application requirements in a distributed microservices environment Develop and optimize large-scale data pipelines for batch and streaming use cases. Ensure data quality, compliance, and governance. Manage and optimize Kubernetes infrastructure, utilizing KEDA to dynamically scale resources for large-scale data processing workloads. Own the full lifecycle of our data stack using Terraform and Kubernetes, from provisioning to application-level frameworks. Provide technical guidance for the team, drive best practices around infrastructure, CI/CD, testing, and system design. Deep-dive into memory management and performance tuning to ensure our large-scale distributed systems are both cost-effective and lightning-fast. What we offer: Comfortable environment, challenging tasks and a long-term interesting project; Mobilization reservation available upon successful completion of the probation period (for Ukrainian colleagues). Covered 20 days of vacation; Working with top notch equipment; Bookkeeping by a professional accountant; Help and support from our caring HR-team;

Location: United States; Canada; Mexico; Costa Rica; Panama; Guatemala About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Role We are seeking a highly skilled Machine Learning Research & Engineering Specialist to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring hands-on experience with Python, machine learning engineering, and coding agents integrated into development workflows. You will build, modify, debug, and validate machine learning implementations, create reproducible training and inference pipelines, and optimize systems for correctness, memory usage, and throughput. The ideal candidate combines strong quantitative foundations with practical coding skills and experience solving complex machine learning engineering problems. SENIORITY: Experienced / Research-Level Engineer EDUCATION: Master's degree or PhD in a relevant quantitative discipline LANGUAGE: Strong written and verbal English LOCATION: Remote — Worldwide Key Responsibilities Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure. Implement or modify model components, data pipelines, evaluation systems, and numerical methods using Python. Build reproducible programmatic workflows using Python, command-line tools, and relevant machine learning frameworks. Debug numerical, algorithmic, and system-level failures to identify root causes and implement reliable solutions. Optimize model training and inference for memory usage, computational efficiency, throughput, and performance. Design and execute tests to verify implementation correctness and compliance with objective performance requirements. Review and validate challenging machine learning engineering tasks, including existing code and proposed solutions. Use coding agents as part of the development workflow to accelerate implementation, debugging, testing, and code review while validating generated results. Document technical approaches, implementation decisions, test results, and performance findings clearly. Deliver high-quality, reproducible task submissions within project deadlines. Requirements Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline. Strong professional or research experience in machine learning and practical ML engineering. Strong Python programming skills, including the ability to implement, debug, test, and optimize non-trivial code. Hands-on experience using coding agents within a Python development workflow. Understanding of machine learning model development, training and inference workflows, and evaluation methods. Ability to build reproducible implementations and verify correctness through systematic testing. Strong quantitative reasoning and problem-solving skills, particularly when diagnosing numerical or computational issues. Ability to work independently, manage technical tasks, and meet defined quality and submission requirements. Strong written communication skills and the ability to explain technical findings. Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.

Location: Brazil; Argentina; Colombia; Chile; Peru; Uruguay About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Role We are seeking a highly skilled Machine Learning Research & Engineering Specialist to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring hands-on experience with Python, machine learning engineering, and coding agents integrated into development workflows. You will build, modify, debug, and validate machine learning implementations, create reproducible training and inference pipelines, and optimize systems for correctness, memory usage, and throughput. The ideal candidate combines strong quantitative foundations with practical coding skills and experience solving complex machine learning engineering problems. SENIORITY: Experienced / Research-Level Engineer EDUCATION: Master's degree or PhD in a relevant quantitative discipline LANGUAGE: Strong written and verbal English LOCATION: Remote — Worldwide Key Responsibilities Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure. Implement or modify model components, data pipelines, evaluation systems, and numerical methods using Python. Build reproducible programmatic workflows using Python, command-line tools, and relevant machine learning frameworks. Debug numerical, algorithmic, and system-level failures to identify root causes and implement reliable solutions. Optimize model training and inference for memory usage, computational efficiency, throughput, and performance. Design and execute tests to verify implementation correctness and compliance with objective performance requirements. Review and validate challenging machine learning engineering tasks, including existing code and proposed solutions. Use coding agents as part of the development workflow to accelerate implementation, debugging, testing, and code review while validating generated results. Document technical approaches, implementation decisions, test results, and performance findings clearly. Deliver high-quality, reproducible task submissions within project deadlines. Requirements Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline. Strong professional or research experience in machine learning and practical ML engineering. Strong Python programming skills, including the ability to implement, debug, test, and optimize non-trivial code. Hands-on experience using coding agents within a Python development workflow. Understanding of machine learning model development, training and inference workflows, and evaluation methods. Ability to build reproducible implementations and verify correctness through systematic testing. Strong quantitative reasoning and problem-solving skills, particularly when diagnosing numerical or computational issues. Ability to work independently, manage technical tasks, and meet defined quality and submission requirements. Strong written communication skills and the ability to explain technical findings. Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.

Location: United Kingdom; Germany; Poland; South Africa; United Arab Emirates; Egypt About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Role We are seeking a highly skilled Machine Learning Research & Engineering Specialist to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring hands-on experience with Python, machine learning engineering, and coding agents integrated into development workflows. You will build, modify, debug, and validate machine learning implementations, create reproducible training and inference pipelines, and optimize systems for correctness, memory usage, and throughput. The ideal candidate combines strong quantitative foundations with practical coding skills and experience solving complex machine learning engineering problems. SENIORITY: Experienced / Research-Level Engineer EDUCATION: Master's degree or PhD in a relevant quantitative discipline LANGUAGE: Strong written and verbal English LOCATION: Remote — Worldwide Key Responsibilities Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure. Implement or modify model components, data pipelines, evaluation systems, and numerical methods using Python. Build reproducible programmatic workflows using Python, command-line tools, and relevant machine learning frameworks. Debug numerical, algorithmic, and system-level failures to identify root causes and implement reliable solutions. Optimize model training and inference for memory usage, computational efficiency, throughput, and performance. Design and execute tests to verify implementation correctness and compliance with objective performance requirements. Review and validate challenging machine learning engineering tasks, including existing code and proposed solutions. Use coding agents as part of the development workflow to accelerate implementation, debugging, testing, and code review while validating generated results. Document technical approaches, implementation decisions, test results, and performance findings clearly. Deliver high-quality, reproducible task submissions within project deadlines. Requirements Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline. Strong professional or research experience in machine learning and practical ML engineering. Strong Python programming skills, including the ability to implement, debug, test, and optimize non-trivial code. Hands-on experience using coding agents within a Python development workflow. Understanding of machine learning model development, training and inference workflows, and evaluation methods. Ability to build reproducible implementations and verify correctness through systematic testing. Strong quantitative reasoning and problem-solving skills, particularly when diagnosing numerical or computational issues. Ability to work independently, manage technical tasks, and meet defined quality and submission requirements. Strong written communication skills and the ability to explain technical findings. Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.

Location: Athens, Greece We are seeking a Semantics/Knowledge Graph Engineer to support a complex platform built around RDF data models, SPARQL queries, and structured datasets. You will be responsible for understanding, maintaining, and evolving the semantic layer of the system, working closely with backend engineers and architects. This role suits a specialist who enjoys data modelling, semantics, and knowledge representation in real-world production systems. What You'll Do: Analyse and maintain RDF/TTL data models and vocabularies; Develop, optimise, and maintain SPARQL queries; Support data ingestion, transformation, and validation workflows; Ensure consistency and correctness of semantic data across the platform; Collaborate with backend engineers to integrate semantic logic into application flows; Assist in documenting semantic models, assumptions, and constraints; Participate in troubleshooting data quality and reasoning issues. Requirements 3+ years of experience working with semantic or data-centric systems; Strong knowledge of: RDF, RDFS, OWL SPARQL Experience with: Knowledge graphs or semantic interoperability platforms; Data modelling and ontology design; Comfortable working with structured data formats: TTL, XML, JSON, CSV Ability to analyse existing models and understand implicit domain logic. Nice-to-Have: Experience with triplestores or graph databases; Familiarity with EU data standards or interoperability frameworks; Python scripting for data processing and/or Apache Airflow; Experience in projects with regulatory or standards-driven constraints. Benefits We believe in rewarding talent and dedication. Here's what you can expect as part of our team: Competitive full-time salary; Private Health Coverage on the Company’s group program; Flexible Working Hours; Top-of-the-Line Tools; Professional Development: Benefit from language courses, specialized training, and continuous learning opportunities; Career Growth: Work with some of the most innovative and exciting specialists in the industry; Dynamic Work Environment: Thrive in a setting that offers challenging goals, autonomy, and mentoring, fostering both personal and company growth. If you want an exciting challenge, work with some of the coolest technologies, and enjoy your time doing it, then join us! Submit your detailed CV in English, quoting reference: ( S/KGE/11/26 ). You may also consider all our other open vacancies by visiting the career section of our website ( www.eurodyn.com ) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading European Software, Information, and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Berlin, Stockholm, London, Nicosia, Valetta, Vienna, Den Haag, Hong Kong, etc.) The company employs over 1200 engineers, IT experts, and consultants (around 3% PhD, 41% MSc, and 54% BSc). We design and develop software applications using integrated, state-of-the-art technology. Our current IT projects have a value exceeding 300 million EURO. EUROPEAN DYNAMICS is a renowned supplier of IT services to European Union Institutions, international organizations, European Agencies, and national government Administrations in 40 countries and 4 continents. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, colour, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published at www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorize ED to process your personal data for the purposes of the company's recruitment opportunities, in line with the Policy.