Вакансии

Опыт коммерческой разработки от 4 лет. Уверенное владение Scala.

Разрабатывать и поддерживать production‑backend для финансовых сервисов, обеспечивать стабильность и отказоустойчивость при реальной нагрузке и сбоях; Реализовывать ключевые сценарии: верификация личности, операции с картами, переводы, кредиты, депозиты, обмен валют;

-Выполнение роли бэкенд-разработчика в команде web-разработки, отвечающей за развитие e-commerce платформы B2B. -Разработка бэкенда B2B-личного кабинета на PHP Laravel.

Location: Москва Workplace: Гибридный Обязанности Ты тот, кого мы ищем, если ты: умеешь формировать видение технического развития и управлять техническим бэклогом; отстаиваешь варианты прохождения технологических (и не только) развилок с помощью понятной аргументации, не прибегая к помощи колюще-режущих предметов; не можешь спать спокойно, если видишь, что пользователь грустит, а показатели продукта (CSI, tNPS, SLA, LT, CFR, и еще много страшных слов) падают; знаешь о мониторинге все, а неисправности в продуктах можешь обнаруживать еще до того момента, как они были реализованы знаешь, как работать с PaaS, и почему это важно для управления требованиями продукта нашел свое призвание в том, чтобы создавать комфортную среду и культивировать общекомандные ценности, построенные на принципах полной открытости; заботишься об индивидуальной траектории развития для каждого члена команды, умеешь менторить, учить, и учиться; играющий тренер на поле информационных технологий, а не повесил бутсы на гвоздь; хочешь стать частью большой команды и быть причастным к титульному продукту МТС (чтобы это не значило) Требования Мы уверены, что ты разбираешься в: способах построения интеграционного взаимодействия с применением BFF/API Gateway (GraphQL/ Gravitee.io ), дуплексных протоколов (gRPC), и чего-нибудь доисторического (например, WCF); принципах SOLID, DRY, KISS, YAGNI, DDD, Onion, Clean, CQRS, MSA, REST, SSDLC, Highload, Fault tolerance, CMMI (нет, это лишнее). React Redux, Redux Saga, Axios SSG/SSR конфигурации Webpack 5+ и сборке сложных приложений Use case/Sequence/ER/Activity diagrams PlantUML/BPMN (но это не точно) OAuth 2.0 React.js 18+ Java 21+ Spring 6+ (Core, MVC, Security Data), Spring Boot 3+, Hibernate, Liquibase Kafka и иные брокеры сообщений PostgreSQL, Redis, ElasticSearch построении CI/CD под GitLab создании CloudReady/CloudNative решения, и их эксплуатации в k8s разнице между GitLabFlow/GitHubFlow/GitFlow и умеешь выбрать оптимальный вариант для конкретной задачи BCP/DRP; Grafana/Zabbix/ELK/Sentry; GitOps/IaaC/Ansible/Terraform; Jaeger/Zipkin/VictoriaMetrics (зачеркни лишнее); Администрировании РСУБД (PostgreSQL), time-oriented хранилищах (ClickHouse) и способах организации кэша; RTO/RPO/SLA/SLO/SLI. Условия Собственную платформу MTS Ocean для получения ИТ-ресурсов, а это значит, что деплой, мониторинг, observability — не будут для тебя проблемой, ты сможешь сосредоточиться на фичах; Профессиональные гильдии инженеров, где мы поддерживаем друг друга и помогаем стать лучше; Внутреннюю площадку TechTalks для обмена опытом, дискуссий, развития навыков самопрезентации; Полезные курсы и вебинары в корпоративном университете и электронную библиотеку;​​​​​ ДМС с первого месяца работы, включая стоматологию; Страхование от несчастных случаев с 1 месяца работы. Материальную помощь в сложных жизненных ситуациях; Отпуск 28 календарных дней (+3 дополнительных дня за ненормированный рабочий день); Мобильная связь за счет компании и льготные тарифы для близких; Подписка на онлайн-кинотеатр KION, сервис МТС Музыка, книжный сервис Строки от МТС, безлимитные мессенджеры и соцсети.

Location: San Francisco; Remote (United States); Seattle; New York City Perplexity serves hundreds of millions of queries a month. Every answer, every Perplexity Computer task, and every enterprise deployment runs on production infrastructure owned by the Cloud Infrastructure team: a fleet of Kubernetes clusters across a multi-account AWS estate, private networks that reach hyperscalers and the neoclouds serving our models, and the storage underneath our user data and agentic workloads. We're looking for SREs who treat reliability as a software problem. You'll own compute, networking, and storage at scale and keep production stable while everything built on top of it is changing fast. RESPONSIBILITIES - Own production at scale. Run the compute, networking, and storage foundation behind Perplexity's consumer, Computer, API, and enterprise products, and own its availability, latency, and cost. - Run our Kubernetes fleet. Operate and upgrade the EKS clusters that host web services, search, datastores, and secure sandboxes. Tune scheduling, autoscaling, node lifecycle, DNS, and service networking so clusters stay healthy under bursty, fast-growing load. - Engineer the network. Design and operate VPC architectures, Transit Gateway and PrivateLink connectivity, load balancing, DNS, and egress across accounts, regions, and cloud providers. - Keep data durable and fast. Own the storage layer (EBS, S3, DynamoDB, and the managed databases and caches our services depend on), including performance, durability, backups, and restores that have actually been tested. - Manage everything as code. Run infrastructure through Terraform and CI/CD. Build reusable modules and guardrails, and make every change reviewable, repeatable, and safe to roll back. - Prepare for failures. Define SLOs, build alerts that someone can act on, design for zone and regional failure, and run game days and disaster-recovery exercises that prove recovery works. - Lead incident response. Be on the critical path when things break. Drive incidents to resolution, write blameless postmortems, and land the fixes so the same failure never pages anyone twice. - Scale ahead of demand. Own capacity planning, quotas, reservations, and cost efficiency so product launches and traffic spikes never wait on infrastructure. - Automate away the toil. Build tooling, increasingly agent-driven, that turns provisioning, upgrades, and remediation into self-serve, hands-off workflows for every engineering team. QUALIFICATIONS We expect you to have real depth in most of these: - Deep AWS expertise. You know VPC, IAM, EC2, EKS, ELB, Route 53, S3, EBS, etc. well past the console, including their limits, failure modes, and the bill that comes with them. - Production Kubernetes at scale: cluster upgrades, CNI, CoreDNS, autoscalers like Karpenter, and debugging a misbehaving node at 3 a.m. Writing Helm charts alone doesn't count. - Terraform mastery. You've designed module structures, managed state across many accounts, and run large refactors and imports without taking production down. - Strong networking fundamentals (TCP/IP, DNS, TLS, load balancing, routing). - You write real code in Python, Go, or Rust for automation, tooling, and controllers, and you'd rather automate a runbook than follow it. - Fluency with observability tools like Datadog, Prometheus, or Grafana, and the judgment to turn metrics, logs, and traces into SLOs and alerts that matter. - A track record of leading production incidents and turning root-cause analysis into durable fixes. - You own problems end-to-end and do well when the path forward isn't laid out for you. ADDITIONAL EXPERIENCE WE VALUE - Multi-region active-active or failover architectures, and disaster-recovery programs with real recovery targets. - Proxies, service meshes, and kernel-level networking: Envoy, Istio, Cilium, or eBPF. - GitOps and progressive delivery: Argo CD, Flux, or similar. - Enterprise and compliance environments: single-tenant or BYOC deployments, KMS/BYOK, SOC 2 or similar controls. - Running stateful systems in production: Postgres, Redis, or search engines like Vespa or Elasticsearch. - Multi-cloud or neocloud operations (GCP, CoreWeave, or similar). - FinOps: cost attribution, Savings Plans and reservations, and making infrastructure cheaper without making it fragile.

Location: San Francisco; Remote (United States); Seattle; New York City Perplexity serves hundreds of millions of queries a month. Every answer, every Perplexity Computer task, and every enterprise deployment runs on production infrastructure owned by the Cloud Infrastructure team: a fleet of Kubernetes clusters across a multi-account AWS estate, private networks that reach hyperscalers and the neoclouds serving our models, and the storage underneath our user data and agentic workloads. We're looking for SREs who treat reliability as a software problem. You'll own compute, networking, and storage at scale and keep production stable while everything built on top of it is changing fast. RESPONSIBILITIES - Own production at scale. Run the compute, networking, and storage foundation behind Perplexity's consumer, Computer, API, and enterprise products, and own its availability, latency, and cost. - Run our Kubernetes fleet. Operate and upgrade the EKS clusters that host web services, search, datastores, and secure sandboxes. Tune scheduling, autoscaling, node lifecycle, DNS, and service networking so clusters stay healthy under bursty, fast-growing load. - Engineer the network. Design and operate VPC architectures, Transit Gateway and PrivateLink connectivity, load balancing, DNS, and egress across accounts, regions, and cloud providers. - Keep data durable and fast. Own the storage layer (EBS, S3, DynamoDB, and the managed databases and caches our services depend on), including performance, durability, backups, and restores that have actually been tested. - Manage everything as code. Run infrastructure through Terraform and CI/CD. Build reusable modules and guardrails, and make every change reviewable, repeatable, and safe to roll back. - Prepare for failures. Define SLOs, build alerts that someone can act on, design for zone and regional failure, and run game days and disaster-recovery exercises that prove recovery works. - Lead incident response. Be on the critical path when things break. Drive incidents to resolution, write blameless postmortems, and land the fixes so the same failure never pages anyone twice. - Scale ahead of demand. Own capacity planning, quotas, reservations, and cost efficiency so product launches and traffic spikes never wait on infrastructure. - Automate away the toil. Build tooling, increasingly agent-driven, that turns provisioning, upgrades, and remediation into self-serve, hands-off workflows for every engineering team. QUALIFICATIONS We expect you to have real depth in most of these: - Deep AWS expertise. You know VPC, IAM, EC2, EKS, ELB, Route 53, S3, EBS, etc. well past the console, including their limits, failure modes, and the bill that comes with them. - Production Kubernetes at scale: cluster upgrades, CNI, CoreDNS, autoscalers like Karpenter, and debugging a misbehaving node at 3 a.m. Writing Helm charts alone doesn't count. - Terraform mastery. You've designed module structures, managed state across many accounts, and run large refactors and imports without taking production down. - Strong networking fundamentals (TCP/IP, DNS, TLS, load balancing, routing). - You write real code in Python, Go, or Rust for automation, tooling, and controllers, and you'd rather automate a runbook than follow it. - Fluency with observability tools like Datadog, Prometheus, or Grafana, and the judgment to turn metrics, logs, and traces into SLOs and alerts that matter. - A track record of leading production incidents and turning root-cause analysis into durable fixes. - You own problems end-to-end and do well when the path forward isn't laid out for you. ADDITIONAL EXPERIENCE WE VALUE - Multi-region active-active or failover architectures, and disaster-recovery programs with real recovery targets. - Proxies, service meshes, and kernel-level networking: Envoy, Istio, Cilium, or eBPF. - GitOps and progressive delivery: Argo CD, Flux, or similar. - Enterprise and compliance environments: single-tenant or BYOC deployments, KMS/BYOK, SOC 2 or similar controls. - Running stateful systems in production: Postgres, Redis, or search engines like Vespa or Elasticsearch. - Multi-cloud or neocloud operations (GCP, CoreWeave, or similar). - FinOps: cost attribution, Savings Plans and reservations, and making infrastructure cheaper without making it fragile.

Location: Almaty, Kazakhstan Workplace: hybrid Geo Search runs the search that tens of millions of customers use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. We are building our own search and recommendation stack, which means we own the data, the ranking, and the measurement. The team is cross-functional, spanning backend, machine learning, mobile, and QA, and works with a product manager focused on relevance and with geo analysts. It owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, and its own search screens on Android and iOS. It is measured on order conversion, search conversion, and mean reciprocal rank We are looking for a Senior Data Scientist to own ranking, relevance and recommendation for geo search: the models that decide which places a customer sees, in what order, and how that changes with context. This is a modeling role with production responsibility, and it is close to a founding one. You will define how relevance is measured here, build the training data from scratch, and own the models that follow. The interesting constraint is that suggestions must return within tens of milliseconds of each keystroke, in cities where map data is thin and people type in mixed scripts, so model choice is an evidence-based trade-off you will own rather than a preference: gradient-boosted ranking on behavioral data today, transformers and large language models where they earn their place in query understanding and cross-script matching, and neural reranking if the failure analysis justifies it. Ground truth comes from what drivers, couriers and customers actually do, and from completed rides, so a large part of the craft is turning messy behavioral logs into labels you can trust. You will measure your impact through offline evaluation and online A/B tests, and changes reach millions of customers in weeks. Key Responsibilities Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact Keep models healthy after launch as data and cities shift Skills, Knowledge and Expertise Minimum qualifications 5 or more years building models that shipped and improved a production metric Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers Experience taking a model to production and owning it afterwards The ability to work across teams, since search quality work generates data and engineering tasks that others own Preferred qualifications Depth in geocoding, autocomplete, or place search Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes Relevance tuning on OpenSearch or Elasticsearch, including analyzers Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid Geo Search runs the search that tens of millions of customers use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. We are building our own search and recommendation stack, which means we own the data, the ranking, and the measurement. The team is cross-functional, spanning backend, machine learning, mobile, and QA, and works with a product manager focused on relevance and with geo analysts. It owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, and its own search screens on Android and iOS. It is measured on order conversion, search conversion, and mean reciprocal rank We are looking for a Senior Data Scientist to own ranking, relevance and recommendation for geo search: the models that decide which places a customer sees, in what order, and how that changes with context. This is a modeling role with production responsibility, and it is close to a founding one. You will define how relevance is measured here, build the training data from scratch, and own the models that follow. The interesting constraint is that suggestions must return within tens of milliseconds of each keystroke, in cities where map data is thin and people type in mixed scripts, so model choice is an evidence-based trade-off you will own rather than a preference: gradient-boosted ranking on behavioral data today, transformers and large language models where they earn their place in query understanding and cross-script matching, and neural reranking if the failure analysis justifies it. Ground truth comes from what drivers, couriers and customers actually do, and from completed rides, so a large part of the craft is turning messy behavioral logs into labels you can trust. You will measure your impact through offline evaluation and online A/B tests, and changes reach millions of customers in weeks. Key Responsibilities Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact Keep models healthy after launch as data and cities shift Skills, Knowledge and Expertise Minimum qualifications 5 or more years building models that shipped and improved a production metric Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers Experience taking a model to production and owning it afterwards The ability to work across teams, since search quality work generates data and engineering tasks that others own Preferred qualifications Depth in geocoding, autocomplete, or place search Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes Relevance tuning on OpenSearch or Elasticsearch, including analyzers Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Annual salary from 70 700 EUR gross Final benefits may vary depending on the location.

Location: Tbilisi, Georgia Workplace: hybrid Geo Search runs the search that tens of millions of customers use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. We are building our own search and recommendation stack, which means we own the data, the ranking, and the measurement. The team is cross-functional, spanning backend, machine learning, mobile, and QA, and works with a product manager focused on relevance and with geo analysts. It owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, and its own search screens on Android and iOS. It is measured on order conversion, search conversion, and mean reciprocal rank We are looking for a Senior Data Scientist to own ranking, relevance and recommendation for geo search: the models that decide which places a customer sees, in what order, and how that changes with context. This is a modeling role with production responsibility, and it is close to a founding one. You will define how relevance is measured here, build the training data from scratch, and own the models that follow. The interesting constraint is that suggestions must return within tens of milliseconds of each keystroke, in cities where map data is thin and people type in mixed scripts, so model choice is an evidence-based trade-off you will own rather than a preference: gradient-boosted ranking on behavioral data today, transformers and large language models where they earn their place in query understanding and cross-script matching, and neural reranking if the failure analysis justifies it. Ground truth comes from what drivers, couriers and customers actually do, and from completed rides, so a large part of the craft is turning messy behavioral logs into labels you can trust. You will measure your impact through offline evaluation and online A/B tests, and changes reach millions of customers in weeks. Key Responsibilities Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact Keep models healthy after launch as data and cities shift Skills, Knowledge and Expertise Minimum qualifications 5 or more years building models that shipped and improved a production metric Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers Experience taking a model to production and owning it afterwards The ability to work across teams, since search quality work generates data and engineering tasks that others own Preferred qualifications Depth in geocoding, autocomplete, or place search Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes Relevance tuning on OpenSearch or Elasticsearch, including analyzers Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets Designed search evaluation pipeline before (Datasets preparation, query/index tuning, search system scores) Worked with and customised Learned-To-Rank plugin on OpenSearch to improve ranking Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

— Разработка и сопровождение Java-приложений и REST-сервисов в микросервисной архитектуре. — Покрытие разработанного кода unit-тестами.

-Развивать и дорабатывать Систему документооборота на PHP и Symfony. -Проектировать и реализовывать backend-логику: жизненный цикл документов, согласования, права доступа, версионирование, аудит действий.

-Разрабатывать приложения, которые масштабируются на все дистанционные каналы банка (интернет-банк, мобильные приложения, платёжные сервисы и многое другое). -2+ года опыта работы с Java 11+ или Kotlin.

Знание PHP, SQL и Symfony, опыт с Symfony в последние годы. Глубокое понимание фундаментальных принципов разработки, способность читать, анализировать и понимать чужой код.

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.

Senior QA Компания: Devquality Локация: РФ Формат работы: Удаленно Зарплата: до 157 000 ₽ на руки Требования Гражданство: только РФ Уверенный TypeScript, опыт разработки и поддержки автотестов для Frontend и Backend от 3 лет.

-Разработка и поддержка серверных приложений и микросервисов на Java. -Проектирование и разработка высоконагруженных REST API.

Location: Brussels, Belgium We currently have a vacancy for a Data Scientist – Text mining / NLP fluent in English, to offer his/her services as an expert who will be based in Brussels, Belgium. The work will be carried out either in the company’s premises or on site at customer premises. In the context of the first assignment, the successful candidate will be integrated in the Development team of the company that will closely cooperate with a major client’s IT team on site and off site. Your tasks Design, implement and optimise advanced AI, NLP, and ML models. Use LLMs, RAG frameworks, and other state-of-the-art approaches; Create methods for tokenisation, part-of-speech tagging, named entity recognition, classification, clustering and other text mining-related tasks; Fine-tune pre-trained models on domain-specific tasks; Conduct thorough research and stay updated on the latest trends and advancements in NLP, ML, and AI technologies; Develop and maintain robust, scalable, and efficient code using Python; Collaborate with cross-functional teams to integrate AI/ML solutions into existing products and services; Perform rigorous analysis and experimentation to improve model accuracy, efficiency, and scalability; Participate in peer reviews and contribute to the continuous improvement of AI solutions; Contribute to the design and implementation of ML application architecture and its solution stack; Develop comprehensive reports and visualisations to communicate insights and findings to stakeholders. Requirements Bachelor degree in IT with focus on Statistics, Data Science, Machine Learning combined with 12 years of relevant working experience ; Experience in Machine Learning and Natural Language Processing; Experience with AI-powered code assistants (e.g., Amazon Kiro, Github Copilot), staying updated with advancements in AI-driven code technologies; Experience with A/B testing and experimental design of ML models; Experience with pre-trained models and LLMs like GPT, and other Transformer-based architectures; Experience with tools like Matplotlib and Seaborn for creating data visualisations; Experience with deep learning frameworks for complex model architecture such as TensorFlow or PyTorch; Proficient in continuous code delivery and unit testing; Excellent knowledge of Python and libraries (e.g. Pandas, SpaCy, NLTK, Hugging Face); Good knowledge of SQL tooling (Oracle, PostgreSQL); Knowledge of NoSQL databases (Elasticsearch, MongoDB); Knowledge of architectural design of scalable ML solutions such as model servers, GPU resource optimization; Knowledge in one of the following areas: predictive (forecasting, recommendation), prescriptive (simulation), topic detection, plagiarism detection, trends/anomalies detection in datasets, recommendation systems; Understanding of bias in ML applications and bias mitigation techniques; Strong understanding of linguistics and text processing techniques; Good knowledge of European Commission grant management ecosystem (eGrants) would be an asset; Excellent command of the English language. Benefits If you are seeking a career in an exciting and dynamic company, where you will offer your services as part of a team of a major European Institution, operating in an international, multilingual and multicultural environment where you can expect real chances to make a difference, please send us your detailed CV in English, quoting reference (158834/11/2026). We offer a competitive remuneration (either on contract basis or remuneration with full benefits package), based on qualifications and experience. All applications will be treated as confidential. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading Software, Information and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Stockholm, London, Nicosia, Hong-Kong, Valetta, etc). The company employs over 600 engineers, IT experts and consultants (around 3% PhD, 36% MSc and 53% BSc or equivalent). We design and develop software applications using state-of-the-art technology. The group generates annual revenues in the range of EURO 40 million, with an EBITDA in the range of 20%. The value of our contract portfolio exceeds EURO 250 million. EUROPEAN DYNAMICS is a renowned supplier of IT services to government institutions, multinational corporations, public administrations and multinational companies, research and academic institutes. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, color, 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 in 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 authorise ED to process your personal data for the purposes of the company's recruitment opportunities, in line to the Policy. Furthermore, when providing your data, it is up to you to explicitly consent that your data can be assessed for future job openings, for as long as you do not withdraw such consent. If you do not consent, we will not be able to consider the data you provide to us for future job openings.

собирать, анализировать и документировать требования от внутренних и внешних заказчиков в Confluence. описывать методы для интеграций, разрабатывать функциональные спецификации и API-контракты совместно с ML- и backend-командами.