Вакансии

Location: Rijeka Workplace: remote CSM Rijeka is looking for self-motivated and skilled seafarers to join our expanding fleet of Columbia Shipmanagement. JOB DESCRIPTION & QUALIFICATIONS   Responsible for leading Engine room team to ensure smooth operation of Engines and machinery equipment. Experience in repairs and dry docking is required. Holder of valid STCW certifications required for the position. Good communication and leadership skills. Effective English communication. Safety culture and mindset. Experience with multicultural working environment.   VESSEL'S SPECIFIC REQUIREMENTS   Minimum Experience in Rank 12 months with Engine type (MAN B&W 2 stroke) Experience on Electronic engines is advantage Substantial experience on large cruise ships with worldwide itinerary. Experience in trading USA area is recommended Holder of valid national COC and C1/D US Visa   CONTRACT TERMS & CONDITIONS   Contract Duration [4 months +/- 1 month] Contract Benefit: Competitive salary  Terms and condition as per CBA applicable Bonus Scheme Free Internet onboard and wellbeing support     You may include with your application any reference, specific job qualification, and your availability date.   Looking forward to welcoming you in the Columbia Group Family.

Location: Ukraine; Czech Republic; Latvia; Spain; Albania; Lithuania; Greece; Bosnia & Herzegovina; Croatia; Estonia; Serbia; Poland; Armenia; Portugal; Italy; Malta; Türkiye; Montenegro; Romania Workplace: Remote Location: Fully remote (EMEA timezone) Start date: ASAP Languages: Fluent English required Industry: Cloud Computing / AI / European Deep-Tech SaaS ABOUT THE ROLE Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers. We are seeking a ML Ops Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications. You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential. YOUR RESPONSIBILITIES - Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent - Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models - Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers - Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance - Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health - Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments - Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities - Define engineering best practices and contribute to platform scalability in a fast-moving startup environment REQUIRED QUALIFICATIONS - 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems - Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent - Strong background in container orchestration and operating GPU-based workloads in production - Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines - Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) - Strong understanding of distributed systems, performance tuning, and production reliability engineering - Ability to effectively use AI coding assistants to accelerate development and debugging workflows - Ownership mindset with the ability to operate independently in a remote-first environment PREFERRED QUALIFICATIONS - Experience with ML platforms such as Kubeflow, MLflow, or KubeAI - Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems - Experience with cost optimization across different GPU types and inference workloads - Background in early-stage startups or greenfield infrastructure projects - Proven experience building production systems from scratch rather than maintaining legacy platforms WHY JOIN US - Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform - Build foundational ML inference systems from the ground up in a high-growth, well-funded startup - Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture - Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems - Influence core engineering decisions and define best practices that will scale with the company. Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.

Location: Ukraine; Czech Republic; Bulgaria; Latvia; Spain; Hungary; Albania; Lithuania; Greece; Bosnia & Herzegovina; Croatia; Estonia; Serbia; Poland; Armenia; Portugal; Italy; Malta; Türkiye; Montenegro; Romania Workplace: Remote Location: Fully remote (EMEA timezone) Start date: ASAP Languages: Fluent English required Industry: Cloud Computing / AI / European Deep-Tech SaaS ABOUT THE ROLE Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers. We are seeking a AI Infrastructure Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications. You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential. YOUR RESPONSIBILITIES - Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent - Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models - Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers - Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance - Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health - Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments - Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities - Define engineering best practices and contribute to platform scalability in a fast-moving startup environment REQUIRED QUALIFICATIONS - 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems - Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent - Strong background in container orchestration and operating GPU-based workloads in production - Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines - Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) - Strong understanding of distributed systems, performance tuning, and production reliability engineering - Ability to effectively use AI coding assistants to accelerate development and debugging workflows - Ownership mindset with the ability to operate independently in a remote-first environment PREFERRED QUALIFICATIONS - Experience with ML platforms such as Kubeflow, MLflow, or KubeAI - Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems - Experience with cost optimization across different GPU types and inference workloads - Background in early-stage startups or greenfield infrastructure projects - Proven experience building production systems from scratch rather than maintaining legacy platforms WHY JOIN US - Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform - Build foundational ML inference systems from the ground up in a high-growth, well-funded startup - Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture - Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems - Influence core engineering decisions and define best practices that will scale with the company. Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.

Location: Ukraine; Czech Republic; Latvia; Spain; Albania; Lithuania; Greece; Bosnia & Herzegovina; Croatia; Estonia; Serbia; Poland; Armenia; Portugal; Italy; Malta; Türkiye; Montenegro; Romania Workplace: Remote Location: Fully remote (EMEA timezone) Start date: ASAP Languages: Fluent English required Industry: Cloud Computing / AI / European Deep-Tech SaaS ABOUT THE ROLE Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers. We are seeking a ML Ops Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications. You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential. YOUR RESPONSIBILITIES - Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent - Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models - Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers - Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance - Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health - Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments - Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities - Define engineering best practices and contribute to platform scalability in a fast-moving startup environment REQUIRED QUALIFICATIONS - 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems - Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent - Strong background in container orchestration and operating GPU-based workloads in production - Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines - Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) - Strong understanding of distributed systems, performance tuning, and production reliability engineering - Ability to effectively use AI coding assistants to accelerate development and debugging workflows - Ownership mindset with the ability to operate independently in a remote-first environment PREFERRED QUALIFICATIONS - Experience with ML platforms such as Kubeflow, MLflow, or KubeAI - Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems - Experience with cost optimization across different GPU types and inference workloads - Background in early-stage startups or greenfield infrastructure projects - Proven experience building production systems from scratch rather than maintaining legacy platforms WHY JOIN US - Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform - Build foundational ML inference systems from the ground up in a high-growth, well-funded startup - Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture - Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems - Influence core engineering decisions and define best practices that will scale with the company. Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.

В рамках данной позиции предстоит заниматься проектированием и разработкой AI-агентов, включая реализацию сценариев, оркестрацию, вызов инструментов и интеграцию с внутренними сервисами. Необходимо преобразовывать прототипы и модели Data Science в стабильные промышленные решения, обеспечивая их упаковку, интеграцию в сервисы и пайплайны, версионирование и сопровождение. Работа включает разработку на Python сервисов, библиотек и пайплайнов, написание автотестов и участие в код-ревью. Также требуется проектировать, создавать и поддерживать витрины данных для ML-моделей и агентов, обрабатывать большие объемы данных с использованием SQL, Hadoop и Spark, оптимизировать запросы и вычислительные задачи. Важной частью является обеспечение качества и наблюдаемости решений через логирование, мониторинг, обработку ошибок и контроль качества данных. Кандидат должен иметь опыт промышленной разработки на Python не менее двух лет, уметь писать поддерживаемый код с типизацией, тестированием, обработкой ошибок и логированием, а также уверенно владеть SQL, включая сложные запросы, оконные функции и оптимизацию. Требуется опыт построения и сопровождения ETL/ELT-пайплайнов и витрин данных, а также опыт вывода сервисов, моделей или data-продуктов в промышленную эксплуатацию. Необходимо понимание принципов работы AI-агентов, включая оркестрацию, вызов инструментов, управление состоянием и интеграцию с API, а также жизненного цикла ML-модели от артефакта и данных до инференса, мониторинга и обновления. Будет преимуществом опыт разработки AI-агентов с использованием LangGraph, LangChain, LlamaIndex или собственного оркестратора, а также опыт работы с LLM, RAG и векторными хранилищами. Дополнительным плюсом считается опыт оркестрации пайплайнов в Airflow или аналогичных системах, работы с Kafka и событийными архитектурами, знание Docker, CI/CD и Kubernetes, практический опыт с Hadoop и Spark, внедрение ML- или LLM-сервисов во внутреннем контуре, а также опыт в банковском домене, продуктах для юридических лиц или документообороте. Формат работы — офис или гибрид в Москве.

Location: San Francisco Workplace: OnSite Company Description We’re http://checkout.comCheckout.com http://Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact. With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started. The ideal candidate thrives in an entrepreneurial environment and consistently outperforms goals. Prior experience in building and scaling platform integrations is essential. Existing platform-specific industry knowledge and network are a valuable bonus.   What you’ll do - Be the technical owner throughout the partner integration lifecycle, including technical discovery with partner, selection and SOW process for System Integrators for building new integrations, and technical project management. - Be the interface between partnerships and product/engineering teams, both internally and externally. - Provide subject matter expertise on platforms for merchants, assist merchant RFPs and technical questions wherever platforms are relevant, and be the escalation point for routing issues to partner technical contacts - Provide input to partnership and product teams on technology trends, integration gaps, future features and platform product roadmaps. - Technical evangelist for platform partners internally and externally. - Build integrations into tech stacks, including APIs. - Run debugging and UAT testing, as well as reviewing code.Act as the core technical owner throughout the partner integration lifecycle, encompassing technical discovery, selection of System Integrators, drafting Statements of Work (SOWs) for new integrations, and technical project management.   Qualifications - 8+ Years experience in a Solutions Engineering, Partner Engineering, Implementation Engineering or similar role (payments industry a bonus). - Knowledge / experience working with eCommerce platforms, subscription management platforms, and ERPs. - Highly familiar with API-based integration methods. - Development experience in frontend and backend web technologies, such as JavaScript, PHP, C# or others. - DevOps experience from a technical and customer solutions point of view. - Strong communicator who can both explain high-level takeaways and drill into technical details with clarity. - Confident working closely with product and engineering teams. - Highly organised and able to prioritise shifting workloads and responsibilities effectively. - Able to thrive in a fast-paced environment. - Fluent in English Additional Information Bring all of you to work We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands. We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here. It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable. Life at Checkout.com http://Checkout.com We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection. Curious about what it’s like to be part of our team? Visit our Careers Page https://www.checkout.com/careers to learn more about our culture, open roles, and what drives us. For a closer look at daily life at Checkout.com http://Checkout.com, follow us on LinkedIn https://www.linkedin.com/company/checkout/life/ and Instagram https://www.instagram.com/checkout_com/

Задачи: — Проектирование и развитие процессов сбора, загрузки, преобразования и передачи данных (ETL/ELT) — Разработка и сопровождение пайплайнов обработки больших объемов данных — Работа с реляционными и нереляционными базами данных — Анализ структуры и качества данных, выявление дублирования, пропусков и несоответствий — Разработка SQL-запросов, процедур, скриптов и механизмов обработки данных — Построение интеграционного взаимодействия между информационными системами и источниками данных — Участие в разработке и развитии DWH / Data Lake / аналитических хранилищ — Настройка процессов регулярной загрузки и синхронизации данных — Обеспечение контроля качества, полноты и целостности данных — Оптимизация запросов и процессов обработки больших объемов данных — Анализ источников данных и определение необходимых таблиц, полей, связей и бизнес-смыслов — Участие в проектировании моделей данных и структуры хранилищ — Взаимодействие с системными и бизнес-аналитиками, разработчиками, архитекторами и DevOps — Подготовка технической документации по потокам данных, интеграциям и структуре БД — Участие в тестировании и приемке интеграционных решений — Поиск и устранение проблем в процессах загрузки и обработки данных — Участие в реализации интеграционных решений между крупными государственными информационными системами Требования: — Опыт работы от 3 лет в роли Data Engineer / инженера по данным — Опыт работы с большими объемами данных и высоконагруженными системами — Уверенное знание SQL и опыт работы с СУБД — Понимание принципов построения DWH, Data Lake и ETL/ELT — Опыт разработки и сопровождения пайплайнов обработки данных — Навыки анализа структуры, качества и взаимосвязей данных — Опыт работы с реляционными и нереляционными базами данных — Понимание принципов интеграции информационных систем — Готовность работать с крупными государственными и медицинскими информационными системами Условия: — Работа над масштабным государственным проектом в сфере цифрового здравоохранения Казахстана — Работа с большими объемами данных и сложными интеграциями — Участие в развитии архитектуры данных и хранилища — Профессиональный рост и развитие компетенций Data Engineering — Взаимодействие с командой аналитиков, разработчиков, архитекторов и DevOps — Оформление по ГПХ / ДВОУ — График 5/2, 09:00–18:00 — Обед 13:00–14:00 — Формат работы: только офис — Адрес: г. Астана, пр. Кабанбай батыра, 17

Location: London, UK About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As an Applied AI Engineer at Anthropic, you will be a trusted technical advisor helping tech-forward companies build on the Claude Developer Platform as they grow and scale. You'll lead deep technical engagements, partnering directly with engineering teams to help them develop their products on top of Claude - advising on implementation, agent design, and working alongside them to build at the frontier of AI. Working closely with Applied AI Architects, Account Executives, and Anthropic's Product and Engineering teams, you'll guide customers through deep technical engagements. You'll leverage your AI engineering expertise to develop custom evaluation frameworks, design scalable architectures, and create the technical resources that enable tech-forward businesses to succeed with Claude. Responsibilities Serve as a deep technical advisor to high-growth, tech-forward companies, working alongside them to build innovative use cases that push the boundaries of AI Work hands-on with customer engineering teams: pair programming, architecture reviews, and code contributions that accelerate their development Develop prototypes and technical documentation—including evaluation suites, AI engineering techniques, and architecture diagrams—that enable customers to build and scale with Claude Collaborate closely with Applied AI Architects to maintain context and continuity across customer engagements Identify patterns across engagements and contribute insights back to Product, Engineering, and the broader Applied AI team Create technical content for developer audiences including documentation, tutorials, and sample code Foster community engagement through hackathons, webinars, technical office hours, and developer-focused events Travel to customer sites for workshops, implementation support, and relationship building You may be a good fit if you have 4+ years of experience as a Software Engineer, Forward Deployed Engineer, or technical founder Experience in a customer-facing or pre-sales technical role - partnering with customers to scope, design, and deliver solutions Production experience building LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale Strong programming skills with proficiency in Python and experience building production applications Experience working with high-growth, tech-forward companies Ability to context-switch across industries (healthcare, fintech, etc.) and use cases Builder credibility that earns trust with technical founders and engineering leaders - you've shipped products and can speak from experience Strong technical communication skills with the ability to translate complex AI concepts into architectural decisions and actionable implementation plans Experience facilitating technical workshops, hackathons, or developer-focused events Passion for making powerful technology safe and beneficial The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £225,000 — £255,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Задачи: — Реагирование и разрешение инцидентов в продакшене (Sev1–Sev4) — Участие в ротации дежурств (1 неделя из 5, отдельная оплата) — Создание и поддержка runbook'ов (управляемых как код с CI валидацией) — Разработка автоматизации и изменений инфраструктуры для клиентских окружений — Настройка систем наблюдения (Datadog, Prometheus, Grafana) — Подготовка постмортемов и техническая коммуникация с клиентами — Внесение вклада в базу знаний — 30–40% времени уделяется инженерным проектам (автоматизация, AI-инструменты, наблюдение) — Участие в развитии AI-усиленного L1 слоя (триаж и автоматизация на основе RAG) Требования: — Опыт работы 5+ лет с Kubernetes, Terraform, AWS/GCP/Azure, Datadog, Prometheus, Grafana, PagerDuty, Linux, Python, Bash, ArgoCD, Helm, CI/CD (GitLab CI / GitHub Actions) — Опыт дежурств в продакшене — обязателен — Опыт Java-разработчика или SRE — Умение работать с AI-агентами (критическое мышление) — Чтение чужого кода и внесение мелких исправлений — Знание SQL — Управление инцидентами (Sev1–Sev4) — Продвинутый английский для прямого общения с клиентами — Внимательность, ответственность, хорошие коммуникативные навыки Условия: — Работа полностью удалённая, только для LATAM (AMER хаб, GMT-3…-6) — Стандартные часы работы 09:00–18:00 по местному времени — Дежурства 1 неделя в ротации с отдельной оплатой (стендбай + оплата за инциденты) — Нет ночных смен, обязательное время восстановления после дежурств — Возможность стать одним из основателей AMER хаба — Реальная автономия, не позиция обработки тикетов — 30–40% времени на инженерные проекты — Работа напрямую с техническим лидом и командой CEMEA

Location: Tel Aviv, Israel About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius is looking for a Senior Data Engineer who in addition to building and owning data pipelines will also drive the design and technical leadership within the data engineering team. This is a hands-on data engineering role, focused on designing, implementing, and maintaining reliable data flows for analytics and machine learning. Infrastructure, cloud, and Kubernetes are used only as tools to run pipelines reliably and cost-efficiently — this is not an SRE or platform engineering role. You’re welcome to work in our offices in Tel Aviv, Israel. Your responsibilities will include: Core Responsibilities (Primary Focus) Design, build, and own production-grade data pipelines using Python and SQL. Develop stateless, idempotent pipelines that are resilient to retries, failures, and infrastructure interruptions. Implement data transformations, validation, and data quality checks. Optimize pipelines for performance, reliability, and cost efficiency. Collaborate closely with Analytics, Data Science, and ML teams to deliver trusted datasets. Supporting Infrastructure (Secondary Focus) Orchestrate pipelines using a workflow orchestration framework (e.g., Airflow or equivalent). Package and run data workloads using Docker and deploy them on Kubernetes. Use autoscaling and Spot / Preemptible compute for efficient pipeline execution. Build CI/CD automation for data pipelines. Use Infrastructure as Code only to provision and manage the infrastructure required to run pipelines. We expect you to have: 8+ years of experience as a Data Engineer, primarily focused on building data pipelines. 6+ years of hands-on experience with Python and SQL. 3+ years of experience running workloads on Kubernetes. Strong understanding of stateless system design and idempotent data processing. Experience building and operating data pipelines in cloud environments. Experience with workflow orchestration frameworks. Strong Linux fundamentals and production debugging skills. Working knowledge of spoken and written English It will be an added bonus if you have: Experience contributing to or working extensively with open-source software. Experience building data pipelines using Apache Spark or similar distributed processing frameworks. Experience building data pipelines that support machine learning workflows. Familiarity with cost-optimized data processing (e.g., Spot / Preemptible compute). Experience with relational and non-relational data stores. Experience working with large-scale or high-reliability data systems. Experience collaborating with strong Data Science and ML teams. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

О компании Aidentity — стартап в сфере AI × Human Intelligence. Продукт, который помогает человеку лучше понять свои сильные стороны и найти свое призвание. Стадия: MVP → развитие продукта → масштабирование Нам нужно: — Опыт работы в стартапе / яркое желание его получить — Готовность влиять на продукт с первого дня — Мышление продуктом, а не выполнение задач — Готовность работать только за долю от компании — ML / Data Science / AI — Python и привычный ML-стек — работа с данными, построением признаков, обучением и оценкой моделей — ML-эксперименты и проверка качества результатов — самостоятельное исследование неизвестной области — интерес к задачам на стыке AI, психологии, человеческие качества и рекомендационные систем — recommendation systems, embeddings, NLP/LLM, clustering, similarity / ranking models. Что предстоит делать ✔️создавать систему синтетических профилей данных ✔️развивать и улучшать алгоритм подбора профессий ✔️проектировать систему, которая будет обучаться на данных пользователей и специалистов ✔️экспериментировать с ML/AI-подходами и проверять гипотезы ✔️оценивать качество рекомендаций и разрабатывать метрики для сравнения разных подходов ✔️исследовать способы персонализации результатов для разных пользователей ✔️в перспективе — работать с NLP/LLM и другими AI-инструментами для анализа и обогащения данных ✔️участвовать в проектировании ML-части продукта и принимать технические решения вместе с командой Что предлагаем: — Удаленка — Гибкий график, part-time — Долгосрочное участие — Опционы — Маленькая команда, где слышат каждого — Продукт с большой миссией Подойдет тем, кто любит эксперименты и не боится неопределенности, готов влиять на будущий результат

Ищем инженера по VoIP и Asterisk, который возьмёт на себя ответственность за поддержку и стабильную работу нашей телефонной инфраструктуры. В задачи входит самостоятельное выявление и устранение проблем, связанных с Asterisk, SIP и VoIP. Требуется опыт работы с Linux и навыки администрирования телекоммуникационных систем. Работа полностью удалённая.

Задачи: — Администрировать и развивать Asterisk / IP-телефонию — Настраивать SIP-транки, endpoints, dialplan и маршрутизацию — Работать с ARI / AMI и интеграциями с backend/CRM — Диагностировать SIP/RTP и проблемы качества связи — Работать с Linux, MariaDB/MySQL, CDR — Настраивать безопасность: Firewall, Fail2Ban, ACL — Поддерживать стабильную работу телефонии и автоматизированного обзвона Требования: — От 2 лет практического опыта с Asterisk — Хорошее знание SIP, VoIP, NAT, RTP, кодеков — Linux + базовый SQL — Опыт с ARI и/или AMI — Умение самостоятельно находить и устранять проблемы — Будет плюсом: Redis, Nginx, Grafana/Prometheus, Heplify Условия: — Формат работы: Remote / Home Office — График: 5/2, 09:00–18:00 — Занятость: Full-time — Зарплата: $3,000 фикс Рассматриваются кандидаты из Армении, Азербайджана, Грузии, Кыргызстана, Молдовы, Узбекистана, Таджикистана.

Требуется Data engineer. Требуемые навыки: — senior — Python.

Location: Singapore; Melbourne; Sydney Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. WHAT YOU WILL DO As an AI Deployment Strategist, AI4Eng for Physics and Engineering in APAC, you are Mistral's forward-deployed operator on strategic enterprise accounts across automotive, aerospace, semiconductors, industrial equipment, and energy. You own the delivery of AI solutions for physics and engineering challenges end-to-end. Discovery, Scoping & Solution Design: - Run technical discovery sessions to understand business and engineering requirements. - Identify, qualify, and prioritize use cases aligned with the customer's strategic R&D and product-development challenges. - Translate business goals into a concrete AI roadmap and high-level solution architecture, validating feasibility with Product and Applied AI teams. - Build ROI models and business value propositions, and own scoping documents and SOW inputs. Delivery & Program Management - Own end-to-end delivery: discovery → design → build → deploy → adoption. - Set up the account operating model: governance, decision forums, and cadence. - Drive day-to-day execution: priorities, milestones, unblockers, and risk management. - Drive scope control and change management, ensuring the right artifacts are produced (design doc, runbook, eval plan, rollout plan, handover). - Bring knowledge of processes and regulations from the industries we serve (aerospace, semiconductor, automotive, industrial equipment, energy). Adoption, Value Realization & Expansion - Lead user testing, training, change management, and developer enablement. - Track value realization against agreed KPIs and report to executive sponsors. - Capture field learnings and feed product gaps and platform needs back to Product, Science, and Engineering. ABOUT YOU - Advanced university degree in physics or engineering, or IT. - 5+ years of experience as a (technical) project manager in a relevant industry, e.g., automotive, semiconductors, or aerospace. - Experience in software development, machine learning, and ideally in numerical simulations for physics and engineering (CAE, CFD, FEM, DEM, etc.). - Hands-on coding proficiency (Python at minimum), with the ability to read design docs and hold your own in deep technical conversations with data scientists, ML engineers, and simulation engineers. - Strong grasp of where AI creates value in computational science: surrogate models, PDE solvers, FEM/CFD acceleration, inverse problems, and differentiable simulation. - Familiarity with deployment patterns (SaaS, VPC, on-prem, hybrid), security and compliance basics, and ideally agile project management methodologies. - Strong communication skills with the ability to explain complex technical concepts in simple terms and to manage C-level stakeholders. We are looking for adaptable team players who excel in a fast-paced environment, are ready to own delivery across APAC accounts, and are prepared to support pre-sales motions to contribute to our shared goals and growth. INTERVIEW PROCESS - Talent Acquisition screening call (30 min) - 30 min Hiring Manager Interview - 45 min Technical Interview (physics, simulation, and AI deep dive) - 45 min Business Acumen Interview - Panel (Customer Meeting Role-Play & Use Case Scoping Exercise, 1h) - 30 min Value Talk - Reference check WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Monterrey, Mexico; Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.CO, Colombia; Rosario, Remote.LATAM-country We are looking for a Data Engineer with a strong technical background and a deep understanding of the data lifecycle from acquisition and integration to modeling and exploitation. The ideal candidate combines technical excellence with business understanding, capable of translating strategic requirements into scalable data solutions that enable decision-making and product development based on reliable and timely information. This person acts as a connector between data architecture, analytics, and business needs, helping the organization evolve toward a data-driven culture. The Data & Analytics organization plays a critical role in enabling data-driven decisions across the company, ensuring information flows efficiently from its generation to its consumption. The team designs and maintains scalable data pipelines and models that power analytical use cases, operational systems, and digital products across LATAM. Operating in a Google Cloud Platform (GCP) environment, the team works with tools such as BigQuery, Dataform, Python, APIs, and Terraform, following Agile and DataOps best practices to ensure reliability, scalability, and business impact. Design, develop, and maintain scalable, efficient data pipelines supporting both analytical and operational needs. Understand the current (“as-is”) state of data sources and architectures, identifying gaps, inefficiencies, and improvement opportunities. Define and lead a data integration and completeness roadmap, ensuring availability, quality, and traceability of information across domains. Translate business requirements into clear technical specifications, collaborating closely with multidisciplinary teams (product, analytics, technology, and business). Implement and optimize data solutions using tools from the GCP ecosystem: BigQuery, Dataform, Terraform, Git, Python, and APIs. Ensure adherence to best practices in version control, documentation, and monitoring of data processes. Actively participate in data project prioritization and planning, balancing business impact and technical feasibility. Promote data governance and literacy, encouraging data-informed decision-making across teams. Mentor peers and contribute to the continuous improvement of data engineering standards. Advanced proficiency in SQL and BigQuery. Experience working with Dataform or similar data modeling frameworks (e.g., dbt). Strong knowledge of Python for data processing and API integrations. Hands-on experience with Infrastructure as Code (Terraform) and version control systems (Git). Deep understanding of the data lifecycle and its impact on digital products and business decision-making. Ability to communicate effectively with both technical and non-technical stakeholders. Experience designing data roadmaps or contributing to technology evolution plans. Proven ability to work in Agile environments, participating in sprints and cross-functional collaboration. Fluency in Spanish (team language); intermediate English for documentation and international collaboration.

Location: Amman, Jordan We are looking to recruit Associate Support Engineers who will receive an intensive training in the field of Support & Implementation and its related technologies and will be involved in providing remote IT technical liaison during the shift, in addition to providing support and network administration to the customers and troubleshooting issues over the phone/emails and onsite whenever needed. Responsibilities: Provide first-line support for the helpdesk and all associated processes. Provide tier-one telephone support for software applications. Provide Basic troubleshooting for software problems covered in training. Utilize help desk software to keep an accurate account of all incoming calls, opened incidents, and requests. Route calls and notify technicians of any critical situation requiring immediate attention. Maintain the proper skills necessary to interface with a variety of users regarding specific problems. Troubleshoot and provide appropriate solutions to problems. Serve as a liaison between customers and the technical department. Maintain client confidence and protects operations by keeping information confidential. Contribute to team effort by accomplishing related results as needed. Perform any other related duties incidental to the work. Requirements: Fresh graduates - up to one year of experience. BSc degree in computer information system or any related field. Willingness to work on different shifts either from (04:30PM - 1:00AM) or (12:00AM – 08:30AM) or (8:00AM - 5:30PM). Willing to travel and work extra hours when needed. Willing to relocate as needed. Excellent written and verbal communication skills (Arabic & English languages). Knowledge of Database engines (Oracle, MS-SQL, Postgres). Knowledge of Operating Systems (Unix/Linux and Windows). Knowledge of Cloud Infrastructure Tools (Docker & Kubernetes) and DevOps Techniques. Knowledge of Application servers (Apache Tomcat, WebLogic, and WebSphere). Familiar with Networking, HA & Disaster Recovery, and Backup & Recovery strategies. Strong customer service and excellent interpersonal skills. Ability to listen and understand what is being described by the end user as well as the ability to explain problem resolutions when necessary. Work with integrity and ethics. Uphold organization values. Show respect and sensitivity for cultural differences.

Основные требования: — Опыт разработки и применения моделей машинного обучения для решения прикладных задач: прогнозирования, классификации, оптимизации, выявления аномалий и анализа производственных процессов. — Понимание основных этапов разработки ML-решений: сбор и подготовка данных, исследовательский анализ, обучение моделей, оценка качества, тестирование и внедрение результатов. — Уверенные навыки программирования на Python и опыт работы с основными библиотеками анализа данных и машинного обучения: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch или аналогичными инструментами. — Опыт работы с временными рядами, историческими данными и построением прогнозных моделей. — Навыки обработки, очистки, объединения и анализа больших объемов производственных и технологических данных. — Общее понимание процессов энергетической отрасли: производство, передача, распределение и потребление энергетических ресурсов, режимы работы оборудования, технологические показатели и показатели эффективности. — Навыки работы с SQL, базами данных и инструментами хранения и обработки информации. — Умение формировать качественные наборы данных для аналитики и обучения моделей машинного обучения. Чем предстоит заниматься: — Разрабатывать и внедрять алгоритмы машинного обучения и интеллектуального анализа данных для задач энергетической отрасли. — Создавать модели прогнозирования производственных и технологических показателей энергетических объектов. — Разрабатывать прогнозы производства, потребления, нагрузки и других ключевых параметров на различных временных горизонтах. — Анализировать технологические и эксплуатационные данные, выявлять зависимости, закономерности, отклонения и потенциальные точки повышения эффективности. — Разрабатывать модели для оценки и прогнозирования состояния оборудования и технологических процессов. — Решать задачи оптимизации режимов работы энергетического оборудования и объектов. — Создавать алгоритмы выявления аномалий, отклонений и нестандартных режимов работы. Что мы предлагаем: — Участие в разработке современных цифровых и интеллектуальных решений для энергетической отрасли. — Работа над практическими задачами цифровизации, аналитики и повышения эффективности энергетических объектов. — Возможность применять технологии Machine Learning, Data Science и Artificial Intelligence для решения реальных производственных задач. — Работа с промышленными, технологическими и эксплуатационными данными энергетических предприятий. — Участие в проектах по прогнозированию, оптимизации, предиктивной аналитике, мониторингу оборудования и развитию цифровых двойников. — Возможность профессионального развития в направлениях Data Science, Machine Learning, Industrial AI и цифровой энергетики. — Взаимодействие с командой специалистов в области IT, анализа данных, автоматизации и энергетики. — Возможность участвовать в проектах, направленных на повышение надежности, эффективности и технологичности энергетической инфраструктуры. — Конкурентные условия труда и возможности для долгосрочного профессионального развития.