Вакансии

• Проектировать, масштабировать и автоматизировать среды (Prod/Stage/Test) через IaC (Terraform, Ansible); • Развивать и поддерживать кластеры Kubernetes и Docker;

-Мобильное тестирование (iOS, Android) в проекте телеком-оператора. -Работа с эмуляторами/симуляторами, чтение логов, анализ трафика.

Автоматизатор на Python превратит повторяющиеся проверки в быстрые, стабильные автотесты. Имеет опыт построения фреймворков автоматизации с нуля с использованием PyTest, Selenium и Appium, а также глубокого тестирования REST API.

Ищем инженера по безопасности ПО от уровня middle, который усилит нашу команду в поиске и устранении уязвимостей. Ключевая задача — настройка и проведение фаззинг‑тестирования с использованием AFL++ и libFuzzer .

Location: Austria; France; Germany; Italy; Netherlands; Poland; Spain; United Kingdom Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today we run our own coding harness on any model you pick, and the router deciding which model answers each request is ours too. The Auto Router Our promise is one enforcement layer for your AI: budgets, spend attribution, and model routing, applied before a request ever hits a model. The Auto Router makes the call: for every request, which model serves it. Eight open-source models whose input prices span more than 20×, cached input up to 50× cheaper than fresh, frontier providers on customers' own keys, and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost: that sentence is the product, and it stays true only if the model inside the router keeps getting better. That model is yours. Made by Cast AI — a Miami-headquartered, globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Every request gets a decision: which model serves it. This is an applied-ML seat, not an infrastructure one. Platform engineers own the proxy, the streaming path, and the fleet. You own the model inside them: the training data, the objective, the evaluation, and the iteration loop that ships a better router every cycle. The problem: routing is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver, what it would cost, and how fast it would decode, and you have to be right enough, in aggregate, that the savings are real and the quality never dips below frontier. The data is messy: rollout outcomes from benchmark harnesses, per-request records from the live fleet, override signals, and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable: on deadline-bound agentic work, a model that decodes three times slower doesn't do the same work slower, it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. And the bar for evidence is high: a router change ships only after production-parity evaluation with error bars, then gets held to its numbers in live traffic. Stack: Python; PyTorch; SQL at scale (ClickHouse); OpenAI- and Anthropic-compatible LLM APIs; benchmark and rollout harnesses (RouterBench-class); GitLab CI. Requirements You've spent 5+ years in applied ML or ML research, and you've trained, evaluated, and shipped models that make real-time decisions (ranking, recommendation, bandits, pricing, or LLM routing) and you can tell that story with receipts. Strong Python and SQL at scale: you can pull, clean, and interrogate millions of request-level records without waiting on anyone. You know the difference between a model that got better and an eval that got easier. You have real opinions about what LLM benchmarks measure, agentic harnesses included. You know enough serving reality (TTFT, decode speed, batching, prefix caching, concurrency) to model the objective honestly. You instrument before you optimize, error bars and all. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically: RouterBench-class evals, cost/quality/latency Pareto fronts. Calibration, abstention, or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Own the routing model end to end: features, objective, training, calibration, and release cadence. Iterate it like a product, not a research artifact. Build the evaluation that gates every change: production-parity benchmarks, router-vs-router comparisons, holdouts by task family, error bars on pass rates. Nothing ships on vibes. Turn fleet data into training signal: per-request outcomes from production, benchmark rollouts, and override feedback, with failure categorization (infra vs task) so poisoned labels never reach the policy. Model the objective the user actually experiences: task success × token cost × wall-clock speed under the real concurrency regime, with cached-input economics in the picture. Run the online loop: shadow, A/B, full rollout, drift monitoring, retrain. When production disagrees with your offline eval, you find out why before the next ship. Push the frontier where it pays: calibrated decisions, abstention under uncertainty, speed-aware routing. We experiment on what a router should be, not just which model it picks today. Partner with platform engineers on instrumentation: the features and labels recorded per request (decode speed first-class among them) and the experiments the data path must support. What’s in it for you? Competitive salary (depending on the level of experience). Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Austria; France; Germany; Italy; Netherlands; Poland; Spain; United Kingdom Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today we run our own coding harness on any model you pick, and we run the models underneath it ourselves. Kimchi Inference One API line and a customer's existing client just works: DeepSeek, GLM, Kimi, MiniMax, Nemotron served serverless from our own GPU fleet (OpenAI- and Anthropic-compatible, pay per token, spend visible per user, team, and key). When control matters, the same models run on the customer's own cluster instead. Behind that one line sits the machinery you'll own: SGLang and vLLM engines, B300 nodes, a shared cluster filesystem that holds every checkpoint exactly once, KV caches riding coding traffic with heavy prefix reuse, and the benchmark harness that gates every change we ship. Made by Cast AI — a Miami-headquartered, globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Throughput. Latency. Cold starts. Cost per token. Move those four numbers in the right direction and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every loader you tune, every scheduler decision you land, every kernel you stare at shows up in a customer's p99 and on our P&L. Your mandate is the inference stack's optimization, whatever it takes, and the technical direction of how we optimize it is yours to argue with data. The problem: the checkpoints run 0.3-1.5 TB, and the "right" serving configuration depends on traffic shape, sequence-length distribution, batch dynamics, GPU SKU, memory bandwidth, and quantization tolerance, all variables that shift week to week. Most teams pick a configuration once, over-provision GPUs, and absorb the cost. We don't: we took Kimi-K3 (1.5 TB) from over an hour cold-to-serving to under twelve minutes with a shared filesystem, O_DIRECT weight loaders, and node-local JIT caches, and we're nowhere near done. Prefill/decode disaggregation and tiered KV caches are next. Stack: Python; SGLang; vLLM; TensorRT-LLM; PyTorch; fastsafetensors; CUDA-adjacent tooling; Kubernetes; virtiofs shared filesystems; ClickHouse; OpenTelemetry; Prometheus; Grafana; GitLab CI. Requirements You've built real ML systems for 5+ years, and your portfolio shows depth in inference or training infrastructure, not just model training notebooks. Your Python is production-grade: services, not scripts. You've run vLLM, SGLang, or TensorRT-LLM for real, and you have a working mental model of why an engine performs the way it does on a given GPU. You've measured quantization regressions, not just compression ratios. Multi-GPU and multi-node setups hold no fear: tensor and expert parallelism, collective communication, and their practical failure modes. You know where the bytes live (page caches, O_DIRECT, network filesystems under terabyte-scale reads) and how fast they move. You instrument before you optimize, and you can tell a real win from a benchmark artifact. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically: RouterBench-class evals, cost/quality/latency Pareto fronts. Calibration, abstention, or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Push throughput. Continuous batching, chunked prefill, speculative decoding with draft models, CUDA-graph capture, and scheduler tuning across the engine fleet. Find the ceiling on each GPU SKU, then raise it. Cut latency. Attack TTFT and inter-token latency separately; profile to find the actual bottleneck (compute, memory bandwidth, scheduling, storage, or network) and fix it, not the one you assumed. Get more out of the KV cache. Hierarchical and paged attention, prefix caching on heavy-reuse coding traffic, eviction policies, quantized KV. A lot of the unrealized throughput lives here, and you'll own it. Kill cold starts. Weight loaders (O_DIRECT, fastsafetensors-class), JIT and compile caches, shared-filesystem read paths. A 1.5 TB checkpoint has no business taking an hour to reach serving. Quantize without regressing quality. INT8/INT4, FP8/FP4 across weights, activations, and KV, measured on real workloads, not just perplexity benchmarks. Scale across nodes. Distributed inference topologies, network-aware placement, prefill/decode disaggregation, checkpointing strategies that don't bottleneck on storage or interconnect. Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments. What’s in it for you? Competitive salary (depending on the level of experience). Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Задачи: Проводить ручное функциональное, интеграционное и системное тестирование. Разрабатывать и поддерживать тестовую документацию, работать с дефектами, анализировать и воспроизводить пользовательские ошибки.

Обязанности: Писать и поддерживать автотесты на Java. Проводить ручное, интеграционное и регрессионное тестирование.

Требования: — Опыт тестирования ПО от 5 лет (включая опыт автоматизации от 3 лет); — Понимание видов и методик тестирования (функциональное, регрессионное, интеграционное, e2e, smoke); — Опыт работы в Agile-командах (Scrum / Kanban); — Уверенные знания TypeScript / JavaScript; — Опыт разработки и поддержки e2e UI тестов; — Практический опыт с Playwright API (APIRequestContext) или аналогичными инструментами; — Умение писать стабильные тесты (работа с ожиданиями, retry, устранение flaky-тестов); — Опыт интеграции автотестов в CI/CD (GitLab CI, GitHub Actions, Jenkins и др.); — Навыки работы с отчетами (Allure, Playwright Report и др.); — Понимание принципов тестирования микросервисной архитектуры; — Понимание HTTP/HTTPS, REST, методов (GET/POST/PUT/DELETE); — Опыт тестирования API с использованием Postman/Swagger/curl; — Умение валидировать JSON/XML ответы (схемы, структуры, данные); — Опыт работы с авторизацией (JWT, OAuth2, cookies, headers); — Навыки тестирования интеграций между сервисами; — Опыт работы с багтрекинг-системами (Jira или аналогами); — Знание SQL и опыт написания запросов (PostgreSQL/MSSQL); — Понимание клиент-серверной архитектуры; — Опыт работы с *nix системами и Windows; — Навыки работы с Docker (будет плюсом); — Опыт работы с SSH; Дополнительно будет плюсом: — Опыт нагрузочного тестирования API; — Базовые знания Node.js; — Опыт менторинга или code review автотестов; — Разработка и поддержка автотестов на TypeScript + Playwright (UI + API); — Анализ логов и взаимодействий между сервисами; — Работа с дефектами.

-Проектирование, разработка и поддержка фреймворка и библиотек для автоматизации тестирования. -Разработка инструментов автоматизации установки и настройки продукта под разные среды выполнения.

Задачи: — Разработка и поддержка автотестов на TypeScript + Playwright (UI + API) — Анализ логов и взаимодействий между сервисами — Работа с дефектами Требования: — Опыт тестирования ПО от 5 лет (включая опыт автоматизации от 3 лет) — Понимание видов и методик тестирования (функциональное, регрессионное, интеграционное, e2e, smoke) — Опыт работы в Agile-командах (Scrum / Kanban) — Уверенные знания TypeScript / JavaScript — Опыт разработки и поддержки e2e UI тестов — Практический опыт с Playwright API (APIRequestContext) или аналогичными инструментами — Умение писать стабильные тесты (работа с ожиданиями, retry, устранение flaky-тестов) — Опыт интеграции автотестов в CI/CD (GitLab CI, GitHub Actions, Jenkins и др.) — Навыки работы с отчетами (Allure, Playwright и др.) — Понимание принципов тестирования микросервисной архитектуры — Понимание HTTP/HTTPS, REST, методов (GET/POST/PUT/DELETE) — Опыт тестирования API с использованием Postman/Swagger/curl — Умение валидировать JSON/XML ответы (схемы, структуры, данные) — Опыт работы с авторизацией (JWT, OAuth2, cookies, headers) — Навыки тестирования интеграций между сервисами — Опыт работы с багтрекинг-системами (Jira или аналогами) — Знание SQL и опыт написания запросов (PostgreSQL/MSSQL) — Понимание клиент-серверной архитектуры — Опыт работы с *nix системами и Windows — Навыки работы с Docker (будет плюсом) — Опыт работы с SSH — Опыт нагрузочного тестирования API (будет плюсом) — Базовые знания Node.js (будет плюсом) — Опыт менторинга или code review автотестов (будет плюсом) Условия: — full-time

Задачи: — Разработка автотестов на Python с использованием pytest/unittest. — Написание запросов для проверки данных в базе данных, включая оконные функции и джоины. — Тестирование API (REST, SOAP) и интеграций с Kafka, Elasticsearch. — Тестирование пайплайнов Spark, Airflow, NiFi, Dolphin. — Создание тест-планов, чек-листов и отчетов о дефектах. — Приоритезация тестовых сценариев на основе бизнес-критичности. Требования: — Гражданство и локация только РФ. — Опыт разработки автотестов на Python не менее 5 лет. — Опыт работы с CI/CD системами (Jenkins, GitLab CI). Будет преимуществом: — Написание unit-тестов на JUnit/TestNG. Условия: — Удалённая работа. — Вилка зарплаты до 180 000 рублей на руки. О компании: — Компания Devquality работает в ИТ-области более 10 лет, занимается разными видами тестирования и поддерживает руководителей проектов в обеспечении качества релизов.

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role We're looking for an Intermediate Site Reliability Engineer to join the Cells Infrastructure team in GitLab's Scale & Architecture organization. In this role, you'll help build and operate Cells, the architecture that lets GitLab.com scale horizontally. Cells splits GitLab.com into many independent units, each with its own application and data stack, behind a shared routing layer. This is a build-it-and-run-it role. You'll provision cells, route traffic to them, deploy them safely, and keep them reliable in production. You'll also help move customer organizations onto Cells, one of GitLab's most important scaling initiatives. What you'll do Operate Cells in production: join the on-call rotation, respond to incidents, lead post-incident reviews, and drive corrective actions to completion. Define and monitor service level objectives (SLOs) for routing and cell health, and build the dashboards, alerts, and runbooks behind them. Provision and operate cell infrastructure on Amazon Web Services (AWS) and Google Cloud Platform (GCP), including Kubernetes, networking, Identity and Access Management (IAM), PostgreSQL, Redis, and object storage. Develop infrastructure as code with Terraform, Helm, and GitOps tooling such as Argo CD. Operate and extend the global routing layer, built on Cloudflare Workers, Envoy, and a Go service backed by Cloud Spanner, and expand routing to more protocols and services. Improve the continuous integration and continuous delivery (CI/CD) pipelines behind ring-based deployments, covering promotion, database migrations, quality assurance validation, and rollback. Support organization migrations by preparing destination cells and helping with dry runs and production cutovers. Work with product teams to make their services cell-compatible, and write documentation so others can operate Cells with confidence. Take part in on-call or operational support where your team’s ownership model requires it. What you'll bring Professional experience running production infrastructure at scale on AWS, GCP, or both, including Kubernetes, along with strong Terraform and Helm skills. Proficiency in Go, or strong experience in another systems language and willingness to work in Go daily. Solid grounding in site reliability engineering (SRE) best practices: SLOs and error budgets, blameless post-incident reviews, reducing toil through automation, capacity planning, and designing for graceful degradation. Experience owning services in production, including on-call, incident response, and monitoring with tools such as Prometheus and Grafana. Practical knowledge of progressive delivery: how to roll changes safely across a fleet and how to roll them back. A strong understanding of distributed-systems fundamentals, such as request routing, proxies, consistency, and failure domains. Experience working effectively in distributed, all-remote teams: you default to asynchronous, written-first communication, document decisions in issues and merge requests, hand off work cleanly across time zones, and stay transparent about progress and blockers. Experience with Cloudflare, Spanner, Argo CD, TypeScript, or operating GitLab at scale is a plus, not a requirement. About the team Every team in our organization owns its services end to end, from design through on-call.We own the control plane that tracks cells and decides where each request belongs, and keep the fleet in step with GitLab's release cadence.We work closely with delivery, database, migration, and product teams across GitLab. Feature teams own how their services behave on the platform; we own the underlying infrastructure.We're an all-remote, asynchronous team. We make decisions in GitLab issues, use directly responsible individuals (DRIs) to own technical direction, demo our work weekly, and use AI-assisted workflows every day. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role Help us build the next generation of authentication services at GitLab. As a Senior Backend Engineer on the Authentication team, you'll build authentication features across the Rails monolith and GATE. GATE is our new service for issuing and validating tokens and managing identities, designed to support every GitLab deployment model. You'll expand authentication mechanisms, including support for identities used by artificial intelligence (AI) agents. You'll also help keep GitLab and its customers secure by building access management tooling and addressing vulnerabilities reported through our HackerOne program. GATE will become the foundation for token issuance, validation, and identity services across GitLab.com multi-tenant software as a service (SaaS), Self-Managed, Dedicated, and air-gapped environments. Your work will affect core components that customers, users, and GitLab teams rely on. Some of our projects include: Building GATE as the centralized service for token issuance and validation Consolidating tokens and migrating authentication paths out of the Rails monolith Building passkeys and passwordless authentication Developing composite identities that combine machine and human authorization for AI Improving service accounts and secure automation Enhancing Security Assertion Markup Language (SAML), Lightweight Directory Access Protocol (LDAP), OpenID Connect (OIDC), and System for Cross-domain Identity Management (SCIM) capabilities for automated user provisioning and single sign-on (SSO) What you'll do Shape the roadmap for GATE and GitLab's authentication services with the team. Design and implement token issuance, validation, and identity services in Go and Ruby across the Rails monolith and GATE, including new authentication mechanisms such as Workload Identity Federation. Migrate authentication and token management paths from the monolith into GATE while maintaining reliability across all deployment models. Build authentication mechanisms from the ground up, such as Workload Identity Federation. Strengthen security for users and organizations through access management tooling and by addressing vulnerabilities reported through our HackerOne program. Define and improve standards for style, maintainability, and engineering practices in a high-scale environment, and advocate for them through code review. Communicate publicly about GitLab initiatives, projects, and contributions from people not working for GitLab Inc. Reflect GitLab's values in these communications. Ship moderately sized features and improvements with moderate guidance and support from other team members, and collaborate on larger projects, including the build-out of GATE. Participate in the maintainer trainee program at your own comfortable pace while working toward becoming a project maintainer. Take part in on-call or operational support where your team’s ownership model requires it. What you'll bring Professional experience with Go and/or Ruby in scaling or mission-critical SaaS products. Familiarity with authentication and authorization technologies or techniques, such as OAuth, OIDC, SAML, SSO, SCIM, LDAP, JSON Web Tokens (JWTs), role-based access control (RBAC), or identity and access management (IAM). Knowledge of token systems, cryptographic signing, and key management. Experience building or contributing to a service from concept to production, including proposal, discussion, and execution. Proficiency in written and verbal English, with the ability to communicate complex problems and solutions clearly in a remote, largely asynchronous work environment. Experience diagnosing, preventing, and resolving performance and optimization problems. Experience working in an iterative software development process, managing your work effectively, staying organized, and following through on priorities. About the team Our mission is to empower GitLab system administrators with the toolkit they need to create their desired balance of security and accessibility for their GitLab experience. Authentication is the first impression a new customer has when they configure a new GitLab instance. We aim to make it as seamless as possible, from that first login to onboarding users and managing security rules for their instance in a secure, flexible, and scalable way. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. 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See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Serbia; Georgia; Montenegro; Kazakhstan We are looking for an experienced Fullstack QA Engineer with a background in fintech, banking or other finance-related products to join our banking QA team. We build mobile banking apps on shared backend APIs. You will test both mobile apps and backend services by hand and help us build test automation for APIs and mobile apps. Our products move real money - cards, transfers, payments - so quality here matters. What You Will Do Test new features and run regression for iOS and Android apps; Test backend APIs and integrations: requests, responses, logs; Design and maintain test cases and checklists in TestRail; Build, maintain and run automated API tests with Postman and Newman in GitLab CI; Maintain and extend our existing Java / REST Assured API test framework (TestRail integration, Allure reporting), and bring it back to regular runs; Contribute to mobile UI test automation for iOS and Android — together with developers in their native test suites or with cross-platform tools — with a focus on end-to-end checks against the real backend; Prepare test data and test accounts for automated and manual runs; Help evolve our test automation approach and tooling; Clarify requirements directly with developers and product managers — we have no business analysts, so you will often find out how things should work yourself; Take part in release checks (release candidate testing, post-release smoke) and help improve QA processes; Use AI tools to speed up your work — and verify what they produce. Requirements 3+ years of experience as a QA Engineer; Experience in fintech, banking, payments or other finance-related products; Solid test design skills: you write test cases and checklists from short task descriptions; Hands-on experience testing mobile apps on both iOS and Android (TestFlight / Firebase builds); Strong API testing skills: HTTP, REST, status codes, authorization; Hands-on API test automation: comfortable reading and extending automated API tests in code (Java or Kotlin preferred) and writing simple tests by analogy - not framework architecture from scratch - plus Postman/Newman (scripts, environments, collections); experience running it in CI; Proxy tools - Charles or Proxyman: inspecting traffic, breakpoints, mocking responses; Working with logs to localise bugs; Strong communication skills and a proactive attitude: you ask questions, dig into unclear tasks and own the result; Readiness to test manually - this is not a pure automation role; Russian - native․ Nice to have Building test automation in code from scratch: framework, CI pipeline, reporting; GitLab CI: pipelines, schedules, artifacts; Basic SQL; Mobile UI test automation: Appium, Espresso/Kaspresso or XCUITest; Experience with feature flags and testing releases with partial rollout. Benefits Fully remote work; Monday-Friday schedule, approximately 10:00/11:00 to 18:00/19:00 Moscow/Cyprus time; A service agreement with a Cyprus or Kazakhstan based company; A fixed monthly salary in euros; Paid annual leave of 21 working days; Paid sick leave; Public holidays: New Year holidays through January 4-6, plus May 1-2 or May 8-9; One additional paid day off per month; The opportunity to attend conferences and take training courses at the company's expense; Challenging and engaging tasks; Training on the company's products and processes; A genuinely comfortable and professional working environment; Opportunities to grow and develop together with the company and its products.

Задачи: — разработка ПО на Python с использованием FastAPI, PostgreSQL — организация интеграционного, unit и регрессионного тестирования — настройка и обеспечение бесперебойной работы, разработки, сборки и дистрибуции программного обеспечения (Gitlab, Docker, etc.) — проработка и внедрение технологий развертывания и обеспечения бесперебойной работы прикладного ПО — развертывание тестовых и основных стендов — взаимодействие с командой по разработке электроники и конструкторами для интеграции программного обеспечения с аппаратной частью. Требования: — высшее техническое образование в области робототехники или смежное направление — отличное знание Python — знание ООП и паттернов программирования — знание сетевых технологий и протоколов (tcp/udp, http, rest api, web socket) — уверенные знания ОС семейства Linux — опыт организации сборки и поставки проектов (Python, Java и С++) для Linux — опыт работы с инструментами CI/CD (Gitlab, Jenkins) и системами управления конфигурациями (ansible, etc.) Будет преимуществом: — опыт разворачивания мониторинга с использованием Zabbix/Prometheus — знание OpenCV и опыт работы с PyTorch и CUDA — опыт работы с Python для разработки робототехнических приложений — общее понимание современных ИИ-инструментов и их возможностей. Условия: — комфортный современный офис г. — Москва, МЦК ЗИЛ, Автозаводская 23а к2 — формат работы - лаборатория робототехники — ежегодный пересмотр зарплаты, годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: — Планировать и сопровождать релизы AI‑агентов и связанных сервисов — Анализировать требования и технические постановки, оценивать тестовое покрытие — Проводить ручное функциональное, интеграционное, регрессионное и E2E‑тестирование AI‑агентов и их интеграций — Тестировать API агентов и вспомогательных сервисов (REST, WebSocket/Socket.io) — Разбирать логи, локализовать ошибки, взаимодействовать с разработкой и аналитикой для быстрого фикса — Вести тестовую документацию: чек‑листы, тест‑кейсы, тест‑планы, сценарии E2E (в Jira/TM4Jira) — Автоматизировать рутинные проверки на Python (pytest + requests), постепенно наращивая долю автотестов в готовом фреймворке автотестирования в CI/CD (GitLab CI / GitHub Actions) — Участвовать в оценке качества ML/LLM‑компонентов: проверка ответов, выявление галлюцинаций, базовые метрики (BLEU/ROUGE, LLM‑as‑judge), регрессионные проверки поведения агентов Требования: — Опыт тестирования 2–4 года: веб‑приложения + API, уверенное ведение тест‑кейсов/чек‑листов, работа в Jira — Опыт участия хотя бы в одном проекте с ML/LLM: тестирование качества ответов, галлюцинаций, базовых метрик (BLEU/ROUGE, LLM‑as‑judge) — будет большим плюсом — Навыки автоматизации на Python: pytest (фикстуры, параметризация), requests для API — Понимание, как встраивать автотесты в готовый фреймворк и запускать в CI/CD — Опыт тест‑дизайна: позитив/негатив/пограничные сценарии, классы эквивалентности, граничные значения — Умение проектировать сложные E2E для микросервисных и диалоговых ML‑продуктов — Понимание ML/LLM‑концепции: классификация, ранжирование, векторизация, поиск, RAG, мультиагентные/агентные системы — Понимание SDLC для ML (data → train → deploy → monitor) — Понимание микросервисной архитектуры: REST, очереди, базовое представление о WebSocket/Socket.io, Redis, Kibana — на уровне чтения логов и понимания, где искать проблему Будет плюсом: — Опыт backend‑автотестов и тестирования Edge сценариев — Опыт настройки тестового окружения, работы с Docker/K8s на базовом уровне — Желание развиваться в сторону SDET / QA‑инженера в AI‑направлении: eval‑фреймворки для LLM, автоматизация оценки агентов, метрики качества Условия: — Гибридный формат работы 3 дня в неделю в офисе и 2 дня дома для Москвы, Санкт-Петербурга, Казани, Ярославля, Рыбинска, Нижнего Новгорода, Воронежа, Краснодара, Самары, Екатеринбурга, Новосибирска, Владивостока — Удалённый формат работы в других регионах РФ — Опыт работы в аккредитованной IT-компании над социально значимыми уникальными проектами — Стабильный доход и квартальные премии — Гибкое начало рабочего дня — ДМС со стоматологией и международной страховкой — Интересная программа внутреннего и внешнего обучения — Прозрачные условия для развития и карьерного роста

Задачи: — участие в создании и поддержке процессов CI/CD — мониторинг состояния серверов и прикладных сервисов — выполнение регламентных работ, заявок и инцидентов в рамках своих обязанностей — анализ и выявление причин возникновения инцидентов — установка новых релизов СПО — работа с реляционными БД преимущественно Oracle, Postgresql — взаимодействие со специалистами технической поддержки пользователей, разработчиками и инженерами ППО Требования: — стек: AIX, Java, Rancher, Docker, Kubernetes, Ansible, Gitlab-CI/CD, Prometheus, Keycloak, Grafana, Zabbix, все Linux-системы, много legacy — написание сценариев автоматизации на Bash и Python — понимание разметки JSON и YAML — работа с системами автоматизации — Ansible — использование Docker — понимание принципов DevOps и infrastructure as code — будет плюсом: опыт конфигурации и уверенное владение Linux (OpenSUSE) — навыки автоматизации CI/CD-процессов (Gitlab CI, Jenkins) — опыт с AIX, Oracle Weblogic будет большим плюсом Условия: — рыночная зарплата и премии — график работы: 5/2 — место работы - бизнес-квартал Арма, ст.м. Курская, Москва — оформление по ТК РФ, полностью белая зарплата — корпоративное обучение и доступ к онлайн-библиотеке — корпоративные тренировки и спортивные активности (бег, футбол, волейбол, баскетбол, теннис и другие виды спорта) — комьюнити по интересам — скидки от партнеров на ДМС, связь, развлечения, подарки, спорт и т.д.