Бэкенд вакансии

Разрабатывать и поддерживать backend компоненты микросервиcов на Go для нашей платформы; Проектировать API (gRPC / REST) и взаимодействовать с базами данных и брокерами сообщений (MongoDB, PostgreSQL, Redis, Kafka);

- PHP/Laravel — от 5–6 лет коммерческой разработки. - Глубокое знание Laravel: очереди, события, service container, Eloquent.

Разработка новых сервисов по ТЗ. Исследование проблем текущих сервисов и их развитие.

Разработка и сопровождение программных компонентов, осуществляющих обработку сетевого трафика (маршрутизация, NAT, Firewall, VPN, DPI, IDS/IPS) в user mode. Оптимизация существующего кода с целью повышения производительности;

разработка и откладка торговых стратегий: портфельное управление, арбитраж, маркет-мейкинг, аукционы, хэджирование на множестве инструментов от акций и бондов до REPO и опционо. разработка движка: внутренний и внешний мессенджинг, генераторы\оптимизаторы кода, конвейер Quaity Gates, система управления конфигурациями и потоками.

Location: New York City Office Workplace: OnSite COMPANY DESCRIPTION Today, when you go to your doctor and get referred to a specialist, your doctor sends out a referral and tells you, “They’ll be in touch soon.” So you wait. And wait. Sometimes days, weeks, or even months. Why? Because too often providers are overwhelmed with the painstakingly tedious work required to get paid by insurance companies. Powered by proprietary models, Tennr handles the complex paperwork that gets patients through the door and providers paid, helping operators get patients the right care, at the right time, in the right setting. ROLE DESCRIPTION We’re looking for a high-agency Senior Backend Engineer to bridge the gap between cutting-edge ML research and real-world product delivery. You’ll design and build agentic workflows that power complex healthcare processes to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. This role involves creating data pipelines, evaluation harnesses, and scalable production-grade agentic systems. You’ll be responsible for researching the right tools and models, ensuring we always use the best-fit technology for each workflow.. As part of our ML team, you’ll work hand-in-hand with researchers to translate new techniques and insights into production-ready systems. The ideal candidate has full-stack engineering experience, hands-on expertise with LLM APIs, and a proven track record of deploying AI systems into production. You’ll work across the stack - from data collection to training pipelines to AI agent orchestration to frontend integration - bringing cutting-edge AI solutions to life in real-world healthcare environments. RESPONSIBILITIES As a Senior Backend Engineer on the ML team at Tennr, you’ll bridge the gap between cutting-edge ML research and real-world product delivery. You’ll help build agentic systems that use LLMs, and vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. - Ship full-stack AI systems end-to-end - Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows. - Go from ideation to code within hours; iterate quickly on experiments and data. - Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and product QUALIFICATIONS - Proficiency in full-stack development, with a strong understanding of system design, backend engineering, and observability infrastructure. - A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures. - Excellent communication and collaboration skills: aligning on interfaces, navigating tradeoffs, and driving cross-team execution. WHY TENNR? - Drive Impact: one of our company values is Cowboy, meaning you set the pace. You won’t just talk about things, you’ll get them done. And feel the impact. - Develop Operational Expertise: learn the inner workings of scaling systems, tools, and infrastructure - Innovate with Purpose: we’re not just doing this for fun (although we do have a lot of fun). At Tennr, you’ll join a high-caliber team maniacally focused on reducing patient delays across the U.S. healthcare system. - Build Relationships: collaborate and connect with like-minded, driven individuals in our Chelsea office 4 days/week - Free lunch! Plus a pantry full of snacks. BENEFITS - Beautiful new office at 345 Hudson Street - Unlimited PTO - 100% paid employee health benefit options - Employer-funded 401(k) match - Competitive parental leave

Задачи: — Развитие и кастомизация внутренних и внешних порталов на базе Bitrix — Разработка функциональных модулей для корпоративных порталов — Реализация интеграционных решений для обмена данными между информационными системами — Обеспечение стабильной работы бэкенда и интеграция с фронтендом на VueJS Требования: — Знакомство с платформой Bitrix / Bitrix24 (архитектура, инфоблоки, API, админ-панель) — Базовые знания PHP — Понимание работы баз данных и владение SQL — Понимание концепции REST API и взаимодействия клиента и сервера — Базовые знания HTML, CSS и JavaScript (jQuery, Ajax) — Владение Git, понимание принципов CI/CD — Знакомство с концепцией асинхронных шин данных и очередями сообщений Условия: — Неполный рабочий день — Возможна удалённая работа

Ищем Laravel-программистов.

Location: San Francisco, CA | New York City, NY | Seattle, WA 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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea. As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience. Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly. Key responsibilities Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention Design resource management and autoscaling so that compute follows demand as a run's needs shift Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build Minimum qualifications Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go Experience designing, building, and operating large-scale distributed systems in production Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally Strong written communication, including design documents and incident writeups Preferred qualifications Experience running ML training or inference infrastructure at scale Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration Experience building schedulers, autoscalers, or resource management systems Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale Experience with high-performance networking, RDMA, or collective communication libraries Experience building observability or automated remediation for large fleets Experience with async Python frameworks such as Trio or asyncio Familiarity with reinforcement learning or large language model training workloads Representative projects Design a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domains Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work Scale environment execution substantially without increasing tail latency for the training step Design an autoscaling policy that rebalances compute across components as a run's bottleneck shifts Build a diagnostics system that explains why a run's throughput dropped and proposes a fix Trace a rare data corruption bug across many services to a race condition in a recovery path, and redesign the path so the class of bug can't recur Design the operational interface for a run so that automated tools can safely diagnose and adjust it under human oversight 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: $500,000 — $850,000 USD 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. 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: — Проектирование и разработка backend-сервисов на Go — Участие в проектировании архитектуры систем и принятии технических решений — Разработка и поддержка API (REST/gRPC) и интеграций с внешними сервисами — Оптимизация производительности и надежности высоконагруженных сервисов — Работа с базами данных и очередями сообщений — Код-ревью, менторинг middle-разработчиков, участие в формировании стандартов разработки Требования: — Коммерческий опыт разработки на Go от 4 лет — Глубокое понимание конкурентности в Go (goroutines, channels, context) — Опыт проектирования микросервисной архитектуры — Уверенная работа с PostgreSQL, понимание индексов и оптимизации запросов — Опыт работы с брокерами сообщений (Kafka/RabbitMQ/NATS) — Опыт работы с Docker, Kubernetes, CI/CD — Опыт написания тестов (unit, интеграционные) Будет плюсом: — Опыт работы с ClickHouse / Redis — Опыт настройки мониторинга (Prometheus, Grafana) — Опыт интеграции LLM/ИИ-сервисов в backend — Опыт работы в аутсорс/аутстафф формате Условия: — Полная занятость (fulltime), удаленный формат работы — Заработная плата от 350 000 ₽ в месяц — Участие в технологических ИИ-проектах — Прямая коммуникация с техническим руководством без бюрократии — Реальное влияние на архитектуру и технические решения

Требования: — 3+ года профессионального опыта в роли Backend Developer. — Уверенное владение Java и Spring Framework. — Владение SQL и проектированием реляционных баз данных. — Опыт работы с NoSQL базами данных, например Redis. — Хорошее знание английского языка. Условия: — Поддержка релокации. — Полная занятость. — Конкурентная заработная плата. — Работа в офисе в Лимассоле. Задачи в описании не указаны.

Разрабатывать и поддерживать бекенд сервисы на Python. Участвовать в разработке архитектуры и концепции продукта.

Обязанности Разрабатывать микросервисы на Go; Проектировать архитектуру новых микросервисов;

SQLAlchemy Тестирование: pytest — обязательное; построение пирамиды тестирования, интеграционные тесты Управление зависимостями: pip / poetry / uv Знание принципов и архитектуры web-сервисов и REST API Проектирование микросервисных решений Уверенная работа с Docker (включая docker-compose);

Задачи: — Разработка и поддержка решений в области финтеха с использованием Python. Требования: — Опыт работы от 5 лет. — Опыт работы с Django, DRF, FastApi. — Плотное взаимодействие с PostgreSQL. — Опыт работы с одним или несколькими из Redis, Clickhouse. — Знание взаимодействия с месседж-брокерами, знакомство с Celery, Kafka или RabbitMQ. — Отличное понимание REST архитектуры. — Понимание работы с асинхронностью (AioHTTP, asyncio). — Умение писать SQL-запросы без использования ORM. — Понимание микросервисной архитектуры. Дополнительно приветствуется: — Знакомство или опыт работы с Oracle DB и SQLAlchemy. — Проектирование или опыт работы с микросервисной архитектурой. — Опыт работы с gRPC. Условия: — Удаленная работа с возможностью гибрида или офиса в Ростове-на-Дону. — Полная занятость. — Тип договора: ИП.

опыт написания операторов/контроллеров для k8s Опыт работы в облаках, хостинге или телекоме Знание систем мониторинга: Prometheus, VictoriaMetrics;

-участие в полном цикле разработки backend (архитектура, кодинг, запуск в промышленный контур). -проектирование и развитие REST API для внешних интеграций.

Задачи: — Разрабатывать и развивать высоконагруженные сервисы и платформенные решения на Python — Проектировать и улучшать архитектуру продуктов вместе с командой — Оптимизировать производительность сервисов и автоматизировать процессы разработки — Работать с базами данных, распределёнными системами и современными инструментами разработки — Участвовать в code review, обсуждении технических решений и развитии инженерных практик — Влиять на развитие продуктов на всех этапах — от проектирования до сопровождения Требования: — Знание Python и понимание принципов его работы — Опыт участия в code review — Понимание многопоточности и асинхронного программирования — Умение писать читаемый и поддерживаемый код — Опыт работы с базами данных и понимание принципов их работы — Знакомство с инструментами наблюдаемости: логирование, метрики, трасировка — Понимание принципов ООП, SOLID и основы функционального программирования Преимущества: — Опыт проектирования распределённых систем и работы с Kubernetes — Опыт внедрения AI-решений в продукты или интерес к AI, LLM и RAG — Участие в улучшении процессов разработки и развитии продуктовых решений