Разработка вакансии - в офисе

установкой, настройкой и администрированием ПО PostgreSOL, MS SQL. образование высшее (программист, администратор баз данных).

• 3+ years of Python development. • Production GenAI / LLM experience • Diffusion models & LoRA.

– Разработка и доработка собственной WMS на 1С. – Автоматизация складских процессов.

— PL/SQL и SQL от 3 лет. — DDL, индексы, триггеры, процедуры, пакеты, представления.

• Leverage AI to design and build features across backend, web, Android, and iOS. • Collaborate with product managers to shape goals into solutions and own the end-to-end development.

Location: Baku Workplace: onsite ABOUT US Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. Why This Role Exists Xsolla is going all-in on becoming an AI-first company, and we're not waiting for a slow rollout to get there. We're offering AI Engineer Internships in regions across the globe to help drive that shift directly, not by sitting in the back office, but by building. You'll work on two things: Internal AI initiatives – helping unlock AI tooling and integrations across Xsolla faster than our existing teams can alone. Proof-of-concept builds on our own APIs – showing our product and engineering teams what's possible when you build with AI as a core part of your workflow, not an afterthought. This isn't a shadow-and-observe internship. You'll ship code that goes to customers – internal teams or external players and game developers – working closely with senior engineers and fellow interns across our global regions. Who You'll Work With You won't be isolated on an intern-only project island. You'll work closely with senior engineers who are actively rebuilding how we write software, and you'll be part of a global cohort of interns building in parallel across our regions. That combination is the point: senior engineers give you a bar for what production-grade, customer-facing code looks like, and your fellow interns give you a peer group to trade ideas, code, and AI workflows every week. What You'll Do Partner and collaborate with senior engineers to identify where AI tooling and integrations can remove friction, and build the fix. Build applications and integrations on top of Xsolla's APIs and SDKs that show internal teams how to move faster using AI-assisted development. Ship work that matters. What you build is expected to reach real users – whether that's an internal tool an engineering team adopts or a customer-facing integration – not a throwaway exercise that gets shelved after your internship ends. Use agentic coding tools (Claude Code, Cursor, or similar) as your default way of working – not a novelty you reach for occasionally. Share what you build. We run weekly 30-second lightning-talk demos where everyone shows how they used AI to solve something that week – you'll be expected to bring something real. Work with minimal process. You'll get context, a problem, and a mentor – the rest is on you to figure out and build. Learn from and collaborate with both directions: senior engineers who'll mentor you on production-grade practices, and fellow interns across markets you'll trade ideas and code with at the weekly demos. What We're Looking For You build things because you want to, and that you already use AI to do it faster. Genuine passion for building. You have side projects, hackathon entries, open-source contributions, or something you shipped just because you were curious. You didn't need an assignment to make it happen. Real AI fluency. You've actually used tools like Claude Code, Cursor, Copilot, or similar agentic coding tools to build something – not just asked ChatGPT to explain a concept. You should be able to explain how you used AI to get there and how you confirmed it actually worked – not someone who fires off a prompt and hopes for the best. Bias toward action. Given an ambiguous problem, you start building before you ask for a spec. Comfort with developing modern full-stack solutions – we care more about how fast you learn than what you already know cold. Open to current graduate students and recent graduates of a Computer Science, Software Engineering, or related program . How to Apply Please include: A link to a GitHub profile, deployed project, or anything else you've built – the more recent and self-directed, the better. A short answer (2-3 sentences, or a 2-minute video/Loom if you'd rather show than tell) to: “What's something hard you built recently, and why did you build it?” We're not grading for correctness or polish. We're looking for whether you have a real answer, and whether it's clear you actually did the work yourself (with AI as your tool, not your ghostwriter). Equal Employment Opportunity Statement: Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.   Criminal History Consideration: For the AI-First Engineering Intern, we will conduct a background check that may include the following: Criminal history check Employment verification Education verification   Relevance to Job Responsibilities: The background check is relevant to this position because of the following role responsibilities: Accessing confidential company data Ensuring compliance with regulatory requirements     Rights Under the Fair Chance Act: Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com.

Key requirements: — .NET — Node.js/TypeScript — Kafka/RabbitMQ — NoSQL databases like Cassandra or MongoDB — Cloud infrastructure Location: — On-site in Porto, Portugal

Чем предстоит заниматься: Анализировать SOTA в области калибровок и смежных областях, проектировать эксперименты, проверять гипотезы и разрабатывать методы калибровки сенсоров. Проектировать CLI/API разработанных методов, автоматизировать процессы калибровки и их тестирование.

Backend-разработчик в БВУ IT Вакансии Развиваем PWA интернет-банкинг и мобильные решения для физических лиц. Ищем Backend-разработчика в команду.

Участие в разработке казначейских продуктов. Высшее техническое и/или экономическое образование.

Задачи: — Разработка и поддержка работоспособности существующего кода системы — Оптимизация производительности системы под высокие нагрузки — Работа с новыми и интересными технологиями (в том числе с ИИ) — Выявление и устранение ошибок систем, улучшение процессов разработки и рефакторинг кода Требования: — Коммерческий опыт в PHP 7 от 2-х лет — Знание принципов ООП и шаблонов проектирования — Знание языка SQL — Опыт использования систем контроля версий git — Высшее образование — Будет преимуществом: разработка под промышленные СУБД (PostgreSQL) — Будет преимуществом: опыт работы в консоли, Linux, опыт работы с ИИ, REST API, GraphQL Условия: — Формат работы: офис — Занятость: full-time

• Управлять полным циклом проекта, защищать интересы акционера перед подрядчиками, регуляторами и партнёрами; координировать работу СП и взаимодействие с китайской стороной.

Обязанности: Внедрение комплексного решения 1С:ERP.УХ с привлечением стороннего Подрядчика (функциональный архитектор 1С ERP.УХ со стороны Заказчика), в том числе: Формирование предложений по улучшению бизнес-процессов Компании и согласование функционально–технических требований к внедряемым информационным системам;

→ Разработка и проектирование PWA интернет-банкинга и мобильных решений. → Микросервисная архитектура.

Разработка и поддержка работоспособности существующего кода системы; Оптимизация производительности системы под высокие нагрузки;

Созданием современных систем беспроводной связи с близкими к предельным характеристиками. Реализация алгоритмов приема и передачи физических стеков радиоинтерфейсов (ЦОС):

Location: San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems. Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated, whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA. We’re looking for a systems engineer who enjoys working deep in the stack. You’ll build production runtime infrastructure in Rust and Go, diagnose difficult Linux and performance problems, and help determine the architecture of Modal’s container platform. You’ll also work closely with maintainers of gVisor and contribute to the runtime itself when the right fix belongs upstream. WHAT YOU'LL WORK ON: - Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads - Build multi-GPU and accelerator-aware snapshotting, including efficient handling of GPU memory and RDMA-enabled workloads - Design zero-copy and low-copy data paths between container memory, filesystems, storage, and Modal’s runtime - Optimize large snapshot pipelines using techniques such as parallel uploads, direct I/O, incremental snapshots, and more efficient memory handling - Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage - Extend our sandboxed runtime to support new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware - Debug complex failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation - Safely roll out runtime and kernel changes across a heterogeneous fleet using compatibility controls, scheduling constraints, feature flags, and observability - Work across the runtime, scheduler, storage, and GPU infrastructure, and take ambiguous production problems from investigation through deployment YOU MAY BE A FIT IF: - You have strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals - You’ve built or debugged container runtimes, sandboxes, the Linux kernel, or similarly low-level infrastructure - You have strong programming ability in Rust, Go, or another systems language, and an interest in becoming productive in both Rust and Go - You’ve profiled and improved systems where memory movement, I/O, synchronization, or kernel interactions dominate performance - You’re comfortable debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel - You can turn loosely defined production problems into well-designed, reliable systems - You want to own important infrastructure and work closely with both internal teams and customers NICE-TO-HAVE: Any of the following would be helpful, but none is required: - gVisor, runsc, runc, OCI runtimes, seccomp, or checkpoint/restore systems - Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying - CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling - RDMA and high-performance networking - FUSE, EROFS, direct I/O, mmap, page-cache behavior, or storage engines - Large-memory or multi-GPU inference and training systems - Contributions to open-source systems software Prior gVisor or machine-learning experience is not required. We care more about strong systems fundamentals, curiosity, and the ability to learn unfamiliar parts of the stack. WHY THIS ROLE: The container runtime is directly on the critical path for inference performance. Improvements here can substantially reduce cold starts, increase token throughput, unlock new accelerator types, and make previously impractical workloads possible. This is an opportunity to work on unusually deep systems problems with immediate production impact. You’ll have room to shape the architecture, contribute to open-source runtime technology, and take ownership of foundational infrastructure used by every inference and training workload on Modal.

Обязанности: Разработка сервисов, утилит и инструментов на C#/.NET; интеграция native ↔ .NET (interop/IPC/API).