Разработка вакансии - удалённо

Разработка и развитие клиентской части продукта на React — от архитектуры и управления состоянием до production-ready UI с реальными пользователями. Проектирование масштабируемой frontend-архитектуры, выбор технологических решений и формирование стандартов разработки.

Проектирование и разработка скриптов и библиотек на TypeScript; Участие в проектировании и улучшении общесистемного функционала;

- Разработка и поддержка высокопроизводительных блокчейн-приложений на языке Rust. - Разработка и оптимизация смарт-контрактов.

• 5+ лет разработки на Ruby on Rails в продакшене. • Опыт проектирования и масштабирования бэкенд-систем.

Synthesia is looking for a Research Scientist Interactive Avatars to develop and evaluate multimodal diffusion models for natural real-time avatar interactions.

Reap is looking for a Senior Software Engineer Treasury to design and operate reliable global money-movement systems for fiat and stablecoins.

Middle/Senior Golang-разработчик Формат работы: Удаленно Зарплата: 250–350тр по итогам технической секции О проекте Мы разрабатываем CRM-систему для среднего бизнеса. Продукт растёт, нагрузка увеличивается, команда расширяется — ищем инженера, который будет проектировать и развивать бэкенд-сервисы.

-Проектировать архитектуру backend'а, чтобы всё работало на максимум. -Писать backend-сервисы.

-Весь функционал соответствует уровню квалификации специалиста (выполняемые задачи соответствующего масштаба, трудоемкости, сложности). -Настройка стандартных и нетиповых конфигураций 1С.

— Развивать antifraud-направление: определять стратегию, roadmap и приоритеты; — Руководить командой scientists/developers и развивать экспертизу;

Обязанности: Создание новых сложных сайтов на WordPress и доработка существующих; Разработка компонентов и модулей, модификация стандартного функционала;

Гибкий график, но с обязательной доступностью в рабочие часы команды и оперативной реакцией на задачи (ежедневные синки на 10-15 минут + присутствие на звонках по типу демо/брейншторм) Что предстоит делать AI-чатботы. Разворачивать с нуля аджентик AI-чатботов для клиентов: подключение к данным каталога, настройка поведения, интеграция на сайт.

Задачи: — Разработка ядра системы потоковой обработки событий и принятия решений в реальном времени. — Совместная работа с ML-специалистами над алгоритмами и прогнозными моделями. — Обеспечение работы алгоритмов в рамках бюджета задержки. — Поддержка архитектуры процессоров, включая управление состоянием, гарантии доставки, партиционирование и репликацию. — Реализация потоковой механики: дедлайны без таймеров, восстановление состояния при рестарте и переезде партиции, повторное проигрывание, идемпотентная выдача команд во внешние системы. — Проверка качества решений на реальных данных и выкат в продакшен. Требования: — Опыт работы на Python от 3 лет. — Техническое образование обязательно. — Понимание основ операционных систем, сетей, распределённых систем и потоковой обработки. — Знание Kafka обязательно. — Знание модели потоковой обработки. — Умение обосновать границы сервиса. — Знание SQL, включая материализованные представления и оконные функции. — Навыки работы с Docker, Kubernetes на уровне пользователя. — Опыт работы с GitLab CI. — Умение работать самостоятельно. — Активное использование ИИ в разработке. — Готовность выполнить тестовое задание и пройти техническое интервью. Условия: — Возможность работать из любой точки мира (удалённо). — ИТ аккредитация. — Программа корпоративных привилегий BestBenifit. — ДМС со стоматологией. — Доступ к обучению английскому языку. — Возможность проходить курсы и тренинги по развитию в сфере специальности. — Участие в интересных и сложных проектах в области искусственного интеллекта.

▫️ Уверенное владение React 18+ (хуки, паттерны композиции, Suspense) от 2х лет. ▫️ Опыт разработки SPA «в бою» (роутинг, guards, state persistence).

• Проектировать и реализовывать критичные компоненты: policy engine, signing orchestration, key lifecycle, recovery flows, approvals, audit trails, multi-tenant wallet/account сервисы. • Строить высоконагруженные multi-tenant SaaS-системы (изоляция тенантов, rate limiting, backpressure и т.д.).

Location: San Francisco, CA; Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ Docker is looking for a Senior Software Engineer to build AI-powered developer tools and the platform behind them on our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building tools that make Docker's engineers more productive and the platform that lets teams across Docker prototype, deploy, and scale their own AI developer tools. As a Senior Engineer you will design, build, and operate: AI-Powered Developer Tools. AI agents and tools that accelerate developer productivity, surface observability insights, and automate operational reviews. You will help teams adopt an AI-native way of working and speed up adoption of tools such as Claude Code and Codex. Examples include AI-powered code review and refactoring assistants, automated test generators and local environment setup tools, deployment pipeline diagnostic agents, and agents that simplify on-call work during incidents. Self-Service AI Developer Tools Platform. Platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools, using the patterns and standards the team sets. You will report to the Senior Manager, AI Developer Tools, and work closely with the team's Principal and Staff engineers, product engineering teams, and platform teams across Docker. WHAT WOULD MAKE SOMEONE SUCCESSFUL IN THIS ROLE You are a strong, hands-on engineer who enjoys building at the intersection of AI, developer experience, and platform engineering. You have built software on top of LLMs and AI agents and have a good sense of what works in practice. You care about the people using your tools, and you can tell a tool developers love from one they put up with. You take a feature from a vague request to production: you ask the right questions about requirements, design it sensibly, ship it iteratively, and operate it afterward. You are comfortable in a fast-moving AI landscape and balance quality with pragmatism. You make the engineers around you better through thoughtful code review and by sharing what you learn. RESPONSIBILITIES Building and Operating AI Developer Tools - Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation - Integrate LLM providers, agent orchestration frameworks, and MCP servers into reliable tools, following the team's patterns and standards - Build systems that help Docker's engineers adopt AI-native development tools - Build integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems - Instrument your tools so the team can measure adoption, effectiveness, and developer satisfaction Self-Service Platform - Build and improve platform capabilities, including deployment pipelines (ArgoCD/GitOps), observability integration (Grafana), security controls, and cost management, that help teams ship their own AI tools faster - Help reduce time-to-production for new products from weeks to days - Contribute to reliability, security, and performance standards for AI systems, including SLOs, monitoring, and incident response Design and Delivery - Take features and services from requirements to production. Ask about scale, users, and constraints before designing, and write down your assumptions - Write clear design documents for the work you own, and participate in design reviews for the team - Review code thoroughly and share what you learn with the rest of the team - Take part in the team's on-call rotation, and help improve alerts, runbooks, and tooling so the next incident is easier - Follow the AI-assisted engineering practices the team sets: use AI tools critically, validate their output, and stay accountable for what you ship Collaboration and Growth - Work with the Senior Manager and the team's Principal and Staff engineers to understand priorities and shape your work - Partner with Infrastructure, Security, and Data teams on integrations and shared standards - Help newer engineers get up to speed on the team's tools, patterns, and practices - Keep up with emerging AI/LLM technologies and agent frameworks, and bring useful findings back to the team QUALIFICATIONS REQUIRED - 6+ years of software engineering experience building and operating production systems - Hands-on experience building LLM-powered applications, AI agents, or AI-assisted developer tools - Working knowledge of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows - Experience building and operating scalable backend services, distributed systems, or developer-facing platforms - Experience with cloud-native infrastructure, including Kubernetes, CI/CD pipelines, and observability systems - Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals - Experience building developer tools, platform systems, or internal tools that other engineers rely on - Good product judgment: you weigh developer experience and technical trade-offs, and you ask clarifying questions before committing to a design - Clear written and spoken communication suited to a remote, async-first company - Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments - Comfortable working autonomously in a distributed, remote-first team - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience PREFERRED - Experience with MCP (Model Context Protocol) or similar AI agent integration standards - Background in developer productivity, DevOps, SRE, or platform engineering - Contributions to open source AI tools, developer tooling, or platform engineering projects - Experience with GitOps deployment patterns (ArgoCD) or Grafana-based observability - Familiarity with security and operational practices for production AI systems - Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure) WHAT TO EXPECT FIRST 30 DAYS - Learn Docker's current AI and developer tooling landscape, the team's services, and the architecture decisions made so far - Meet your teammates, including the Principal and Staff engineers, the Senior Manager, and key stakeholders in product engineering - Ship a useful production change in your first weeks, using the team's AI-assisted engineering workflow - Review existing AI tool prototypes and internal experiments to learn what is working and what is not - Shadow on-call and learn the operational profile of the team's services FIRST 90 DAYS - Own a feature or service from design to production with measurable impact, such as part of an AI code review agent, a deployment diagnostic agent, or an on-call assistant - Write the design document for work you own and take part in team design reviews - Contribute to the self-service platform's deployment pipeline, observability integration, or security controls - Take part in the on-call rotation FIRST YEAR OUTLOOK - Be a go-to engineer for one or more of the team's AI tools or platform components - Have shipped multiple production improvements that Docker engineers use every day, with adoption and effectiveness you can point to - Have helped teams outside AI Developer Tools build and run their own tools on the platform - Have taken on larger and more ambiguous work as your scope grows, and helped newer engineers do the same Docker considers visa sponsorship on a case-by-case basis based on business needs.   Compensation & Equity United States: $160,900 – $260,700 + equity ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Location: European Union Workplace: Remote 🌍 Remote | UTC+8 | Core collaboration hours: 1:00–7:00 PM Manila Time   Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines. We combine global fintech expertise with deep local market knowledge to make financial services simple, accessible, and useful for millions of people across the region. 7M+ app downloads. 2M+ monthly active users. 7,000+ partner stores. US$310M+ raised from leading global investors. Manila-based, globally distributed, and hybrid-first — our team spans 45+ countries. If you want to solve complex problems at scale and impact how millions of people access and manage money, come build with us. Southeast Asia's fintech moment starts here. What’s special about our payment platform: - Built for resilience: a fault-tolerant platform where payments keep flowing even when parts of the system or external partners fail - Integrations: international partners and domestic Philippine payment rails - Money-movement flows: cash-in, cash-out, bill payments, loan disbursement and repayment, and more - Platform approach: one platform serving multiple internal products and teams, with shared APIs, SDKs, and reusable building blocks - Architecture: Kotlin/Spring Boot microservices, Kafka, relational databases, and durable workflow orchestration for long-running payment processes - Reliability by design: idempotency, retries and timeouts toward unreliable external partners, full audit trail - Compliance: regulated environment (BSP), with security and fraud checks built into the flow What you’ll do: - Develop scalable microservices on the JVM with Spring Boot, as part of a team building platform solutions for our banking product. - Design payment services with a focus on DDD, eventual consistency, and resilience to partner and infrastructure failures. - Work with product, QA, security, and operations to release changes under agreed engineering standards. What you’ll own: - Own the design, build, and maintenance of software components supporting the Core Engineering team. - Design and evolve payment flows end to end: processing, routing, status handling, refunds/reversals. - Integrate new payment providers, banks, and partner channels, and handle their failure modes gracefully. - Build and maintain scalable, reliable APIs and services, and contribute to internal SDKs and frameworks that let product teams launch new payment use cases quickly and safely. - Investigate payment incidents (stuck, duplicated, or inconsistent transactions) and make the system self-healing. - Translate functional requirements into technical solutions and maintainable implementations. - Contribute to code and configuration reviews. What makes you a strong fit: - 3+ years of software development on the JVM; good knowledge of Kotlin is an advantage - Experience with Spring Boot in a microservice environment - Experience with domain-driven design and eventual consistency - Experience with payments, banking, or other transactional/financial systems (payment platforms, PSP integrations, wallets, ledgers) - Solid understanding of idempotency, distributed transactions, retries, and timeouts in money flows - Track record in an agile setup with continuous deployment and delivery - Experience with containerization and orchestration in a cloud environment (Docker and Kubernetes preferred, AWS preferred) - Experience with, and enjoyment of, continuous knowledge-sharing within the team - Nice to have: Kafka, workflow engines, ISO 20022 What we offer: Ownership and flexibility - Fully remote work with core collaboration hours from 1:00 to 7:00 PM Manila time (UTC+8) - Company-provided tools and equipment Health and time off - Medical insurance support for you and your family through co-funding or reimbursement, depending on your location and subject to policy limits - Access to an internal mental health support specialist - 22 vacation days, Philippine public holidays, and 15 sick days Growth and team experience - Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications - Company-sponsored trips to Manila to meet and work with your team in person - High-performing teams can earn a dedicated beach house week in Southeast Asia   We believe strong teams are built by people with different backgrounds, experiences, and points of view. Salmon is an equal opportunity employer, and we make hiring decisions based on skills, experience, and potential.

Location: San Francisco, CA; Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ Docker is looking for a Staff Software Engineer to set the technical direction for key parts of our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building the AI-powered tools and the platform that let teams across Docker prototype, deploy, and scale their own AI developer tools. As a Staff Engineer you will own the technical design and delivery for two areas: AI-Powered Developer Tools. Design and build AI agents and tools that accelerate developer productivity, surface observability insights, and automate operational reviews. You will help teams adopt an AI-native way of working and speed up adoption of tools such as Claude Code and Codex. Examples include AI-powered code review and refactoring assistants, automated test generators and local environment setup tools, deployment pipeline diagnostic agents, and agents that simplify on-call work during incidents. Self-Service AI Developer Tools Platform. Design and build the platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools. You will set the patterns and standards that let teams experiment, iterate quickly, and graduate successful prototypes into production-ready services. You will report to the Senior Manager, AI Developer Tools, and work closely with the team's Principal engineers, engineering leadership, product engineering teams, and platform teams across Docker. WHAT WOULD MAKE SOMEONE SUCCESSFUL IN THIS ROLE You are a hands-on technical leader who works well at the intersection of AI, developer experience, and platform engineering. You have shipped production systems built on LLMs and AI agents, and you can explain what worked and what did not. You think in platforms: you design something once so that several teams can move faster. You have clear opinions about what makes a developer tool good, and sound judgment about when to build, when to integrate, and when not to do either. You are comfortable in a fast-moving AI landscape and balance technical quality with shipping iteratively. Before you design, you ask about scale, users, and constraints rather than assuming them. You lead through influence and mentorship, and the engineers around you get better because you are on the team. RESPONSIBILITIES Technical Leadership and Architecture - Set technical direction and own the architecture for AI-powered developer tools and the self-service platform within the team's roadmap - Turn ambiguous problems into clear requirements, plans, and staged decisions. Ask about scale, users, and constraints before designing, and write down your assumptions - Establish patterns, standards, and best practices for LLM integration, agent development, and production AI systems that the team and partner teams adopt - Shape platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools - Design reliable, scalable infrastructure for hosting AI agents and developer tools - Recommend AI technology choices, LLM provider strategies, and agent orchestration approaches, and make the trade-offs explicit - Work with the Senior Manager and the team's Principal engineers to align technical decisions with business goals Systems Design and Implementation - Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation - Build self-service platform capabilities that reduce time-to-production for new AI tools from weeks to days - Build systems that speed up adoption of AI-native development tools (Claude Code, Codex) across Docker's engineering organization - Set and meet reliability, security, and performance standards for AI systems, including SLOs, monitoring, incident response, and cost management - Design integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems - Take part in the team's on-call rotation and use what you learn to improve alerts, runbooks, and tooling Strategic Impact and Innovation - Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks, and recommend what Docker should adopt and what it should not build - Define how we measure AI tool effectiveness, including adoption, productivity gains, and developer satisfaction - Improve AI tool performance, cost efficiency, and developer experience through architectural improvements - Contribute to the technical approach for hardening proven internal tools so other teams across Docker can adopt them - Lead cross-team technical delivery, surface risks early, and work with Infrastructure, Security, and Data on shared standards Leadership and Mentorship - Mentor senior engineers on AI/LLM integration patterns, agent development, and platform engineering practices - Lead design reviews and raise the quality of design and code reviews across the team - Create reusable patterns, documentation, and architectural decision records (ADRs) that other engineers build on - Help the team turn good individual AI-assisted engineering practice into shared workflows and controls - This role may require participation in an on-call rotation to provide support outside of standard business hours, including evenings, weekends, and holidays, as needed. QUALIFICATIONS REQUIRED - 8+ years of software engineering experience, with demonstrated technical leadership on production systems at scale - Hands-on production experience building LLM-powered applications, AI agents, or AI-assisted developer tools - Strong understanding of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows - Track record of designing and building scalable distributed systems and developer-facing platforms - Production experience with cloud-native infrastructure, including Kubernetes, GitOps deployment patterns, observability systems, and CI/CD pipelines - Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals - Experience designing developer tools, platform systems, or internal tools that other teams rely on - Track record of clarifying requirements and constraints with stakeholders before committing to a design - Track record of leading complex initiatives that crossed team boundaries, and of influencing without direct authority - Strong product and platform judgment: you weigh business outcomes, developer experience, and technical trade-offs - Clear written and spoken communication suited to a remote, async-first company - Track record of mentoring other experienced engineers - Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience PREFERRED - Experience with MCP (Model Context Protocol) or similar AI agent integration standards - Background in developer productivity, DevOps, SRE, or platform engineering - Contributions to open source AI tools, developer tooling, or platform engineering projects - Experience evolving an internal platform or tool into a widely adopted, production-grade service - Knowledge of security, compliance, and operational practices for production AI systems - Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure) WHAT TO EXPECT FIRST 30 DAYS - Build context on Docker's current AI and developer tooling landscape, including existing AI experiments, LLM provider relationships, and architecture decisions to date - Meet the Senior Manager, the team's Principal engineers, engineering leadership, and key stakeholders in product engineering to understand needs, pain points, and constraints - Review existing AI tool prototypes and internal experiments to learn what is working and what is not - Get hands-on with the team's services and ship a useful production change - Shadow on-call and learn the operational profile of the team's services - Work with the Senior Manager to agree on initial technical priorities FIRST 90 DAYS - Lead the technical framing and delivery plan for one significant problem, from requirements through to production - Document the architecture, technology choices, and integration patterns for that work, including ADRs - Ship a production AI developer tool that sets patterns the team can reuse (for example an AI-powered code review agent, a deployment diagnostic agent, or an on-call assistant) - Contribute to the technical foundations of the self-service platform: deployment pipeline, observability integration, security controls, and cost management - Lead design reviews for your area of the roadmap and begin mentoring other engineers - Take part in the on-call rotation FIRST YEAR OUTLOOK - Be the trusted technical lead for your area of AI Developer Tools, and a sought-after partner for teams across Docker - Deliver at least one material outcome that spans multiple teams or systems - Have multiple production AI tools demonstrating value across Docker's engineering organization - Help build a self-service platform that lets multiple teams build, deploy, and operate their own AI tools with little friction - Define and instrument how the team measures AI tool adoption, effectiveness, and developer productivity impact - Grow other senior engineers into broader ownership, and reduce the number of decisions that depend on a small set of people Docker considers sponsorship on a case-by-case basis based on business needs.   Compensation & Equity United States: $170,350 – $275,550 + equity ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE