Разработка вакансии - удалённо

-Разработка на языке Rust программного обеспечения для высоконагруженных серверных приложений. -Реализация API для бэкенд-сервисов.

-Опыт работы в области Frontend developer (React) от 3 лет. -Отличное знание языков JavaScript, Typescript.

— Design, build, and maintain scalable distributed backend systems powering platform and product capabilities. — Own backend services end-to-end: from architecture and implementation to monitoring and long-term reliability.

NestJS for 2+ years — modules, guards, interceptors, microservice transports. • Deep PostgreSQL: schema design, indexing, EXPLAIN ANALYZE, zero-downtime migrations, database-per-service pattern.

На долгосрочный проект ищу Frontend Developer. Stack: JavaScript, TypeScript, React, CSS3, HTML5, WebSockets, WebGL, PixiJS, Spine.

Бэкенд на Go — микросервисы, аутентификация, API-ключи, сборщики данных — и исследования: почему резолвер ответил так, где протекает прокси, что видно по DNS-запросам. Про многое из этого в интернете не написано — придётся ставить стенд и смотреть.

Грейд: Senior. Стек: PHP, Laravel, WebSockets, Server-Sent Events, RabbitMQ, Kafka, SQS, Redis.

Задачи: — Разработка нового проекта в команде Intercity — Участие в процессе Scrum Требования: — Kotlin, Jetpack Compose, Coroutines, MVVM/MVI — 2+ года коммерческой разработки под Android Условия: — Работа по Scrum — Возможность работать на разных рынках

• Вести задачи полностью — от идеи до продакшна. • Разрабатывать и развивать компоненты платформы.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY 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 Anthropic's Legal Tech engineers play a specialized and critical role in keeping Claude at the frontier. Partnering closely with counsel, we provide custom infrastructure and guidance for complex, novel technical matters, empowering teams across the company to continue meeting our legal obligations as we innovate. As a Senior+ Software Engineer on our Legal Tech team, you'll build dedicated, highly secure, and reliable cloud environments and data processing and storage capabilities at speed and scale. You will own your scope end to end: from raw requirements all the way through to large-scale production systems that satisfy legal invariants and unlock new possibilities. The systems you build won't just be dependable and scalable; they'll be interpretable and auditable, demonstrating to skeptical audiences exactly how they worked and why results can be trusted. You'll be an essential member of a collaborative team, working together to solve difficult problems against fixed deadlines. You will own internal customer relationships as well: you'll gather, prioritize, and coordinate needs from colleagues on Privacy, Security, Research, and/or Infra as well as Legal, to ship the right capabilities on the right timelines. You'll act as a trusted advisor: clearly explaining technical concepts and constraints to inform legal decisions, and proactively designing new approaches to prevent classes of problems. Your infrastructure expertise will directly empower our business to calibrate and manage risk, innovating faster thanks to systems that build in legal considerations. Key responsibilities Build and operate dedicated cloud infrastructure, storage, and data pipelines for specialized workloads that can't run on general-purpose systems Design architectures with excellent security, privacy, reliability, scalability, and auditability baked in Partner with a wide range of internal customer teams to translate requirements into sound approaches and realistic ETAs Deliver against fixed deadlines Support complex and novel legal requests on controlled infrastructure, with verifiable chain of custody Pair with engineering teams across the company to extend existing infrastructure to support new workflows and/or legal requests Work with nontechnical stakeholders to clarify their needs through responsive, easy-to-follow dialogue Minimum qualifications Experience designing, building, and operating highly trusted infrastructure and data systems in production, including owning architectural decisions Experience working across large, complex, multi-team environments, including in stacks and code you don't own Ability to take ownership of a cloud account and secure it end to end (networking, storage, identity, encryption, least-privilege provisioning, etc.) on AWS and/or GCP Ability to translate legal and privacy requirements into sound technical designs and implementations that teams are excited to adopt A habit of verifying rather than assuming: designing checks for invariants and plainly naming what isn't proved Self-directed in a high-change environment, and comfortable resequencing quickly when new needs emerge Clear communication with nontechnical stakeholders, and an understanding of their needs Preferred qualifications Software engineering experience with an infrastructure, data, privacy, and/or security focus Proficiency with large-scale data tooling, such as distributed processing, lakehouse table formats, and/or query engines Understanding of data processing pipelines, LLM training, and/or provenance and lineage Experience with secure enclaves, confidential computing, clean rooms, or other purpose-built environments for sensitive data Background in eDiscovery, litigation support, or records retention at scale, including chain of custody and verifiable integrity 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: $320,000 — $485,000 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.

Участие на проектах по внедрению 1С ЕРП (ERP). Доработки по ТЗ.

- Рефакторинг кодовой базы, переписывание на Clean Arhitecture и постепенное выделение сервисов; - Обеспечивать масштабируемость и надёжность платформы при росте нагрузки;

Задачи: – проектирование и разработка пользовательских или библиотечных функций по функциональным заданиям для конфигурации 1С: WMS Логистика. Управление складом, редакции 5.0; – формирование или участие в ревью технических заданий для сложных функций; – разработка интеграций системы с базами 1С и другими системами компании. Требования: – хорошее понимание возможностей и опыт разработки в конфигурации ERP 2.5 контур оперативного учёта, работа с ордерными складами; – знание возможностей БСП в контексте разработки нового функционала конфигураций; – понимание принципов интеграции ИТ-систем и способов обмена данными между ними, в том числе с использованием REST API, http сервисов; – понимание принципов разработки типовых конфигураций, принципов тестирования и поставки программного обеспечения; – опыт работы от 5 лет в роли разработчика на платформе 1С: Предприятие. Будет плюсом: – понимание технической архитектуры 1С: WMS Логистика. Управление складом, редакции 5.0 (Axelot / Ситек) и опыт разработок для этой конфигурации; – опыт подключения и настройки складского оборудования – ТСД, сканеры ШК, принтеры этикеток; – понимание принципов DevOps, опыт работы с GIT; – опыт работы с КД2.

Location: Andorra la Vella, Andorra; Buenos Aires, Argentina; Brasilia, Brazil; Rio de Janeiro, Brazil; Barcelona, Spain; Madrid, Spain; Milano, Italy; Rome, Italy; Abu Dhabi, United Arab Emirates; London, United Kingdom; Lugano, Switzerland; Warsaw, Poland; Amsterdam, Netherlands; Lisbon, Portugal; Dublin, Ireland; Bucharest, Romania; Bangalore, India; Kuala Lumpur, Malaysia Workplace: remote Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications. You will work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate data such as text, images, and audio. We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments. The ultimate goal is to enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios. Responsibilities Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms. Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint. Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource-constrained devices. Document iterative results and compare outcomes against established benchmarks to validate performance across platforms. Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges, specifically those encountered on low-resource devices. Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions. Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics. Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource-constrained systems. Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define clear success metrics such as improved real-world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements. A degree in Computer Science  or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Must have knowledge of Metal Shading Language (MSL). You should be comfortable writing custom compute shaders from scratch. Proven experience in low-level kernel optimizations and inference optimization on mobile devices is essential. Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications, particularly on resource-constrained devices and edge platforms. A deep understanding of modern model serving architectures and inference optimization techniques is required. This includes state-of-the-art methods for achieving low-latency, high-throughput performance, and efficient memory management in diverse, resource-constrained deployment scenarios. Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end-to-end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource-constrained devices is required. Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints. You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency. Distributed Inference Systems: Designing and optimizing high-performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters. Deep understanding of the math and structure behind Diffusion Models and Vision Transformers Understanding of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc. Important information for candidates Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles: Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/ Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website. Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. Double-check email addresses. All communication from us will come from emails ending in @ tether.to or @ tether.io We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. When in doubt, feel free to reach out through our official website.

Click&Boat is a rental platform product of Boats Group, the fastest-growing digital business in the recreational boating space.

instinctools is a software development company that provides custom software solutions for businesses of all sizes. Remote work (EU, Poland, Kazakhstan, Serbia, Georgia).

Location: Remote (U.S. or Canada) About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future! About the Role: Cantina is a place where people create AI characters and the videos those characters star in, and the catalog grows far faster than anyone can browse. Search and recommendations are what turn that catalog into a product: they decide which characters a new user meets in their first minute, which creators get discovered, and what appears in every feed, leaderboard, and discovery surface we ship. We are hiring a Senior Staff Software Engineer to own that area end to end — retrieval, ranking, relevance quality, and the roadmap for all three. This is the first dedicated hire for search and recommendations, so you will inherit a real production system serving live traffic along with a large amount of unclaimed ground, and you will set the technical direction rather than execute someone else’s. What you'll do: - Own the architecture and roadmap for search and recommendations end to end — query understanding, retrieval, ranking, and the surfaces they feed: search, discovery, the home feed, and our trending and creator leaderboards. - Make relevance a measured discipline rather than an opinion: define the quality metrics per surface, build the offline evaluation and golden-set tooling, and run the online experiments that decide whether a ranking change ships. - Design and evolve the ranking stack — engagement signal pipelines, decay and freshness models, and the pattern we use to combine pre-computed index-time signals with live re-ranking at query time. - Own OpenSearch in production: index and mapping design, reindex and cutover safety, query performance, cost, and the operational headroom of the clusters behind every discovery surface. - Build candidate generation and personalization for recommendation surfaces, and partner with data and ML on the feature pipelines and models behind them. - Work directly with Product and Trust & Safety on what “good” means for each surface — quality gates, cold-start behavior for brand-new creators and characters, and what must never be recommended. - Set the engineering standard for the area: instrumentation, flag-gated rollouts, and runbooks that let other teams change ranking behavior without breaking it. - Act as the technical point of contact for discovery across backend teams, and mentor the engineers whose work touches it. What you'll bring: - 10+ years building production software, with substantial depth in search, ranking, recommendations, or ML-driven relevance on a consumer-scale product. - Hands-on expertise with a production search engine — OpenSearch, Elasticsearch, Lucene, Vespa, or similar — covering index and analyzer design, relevance tuning, and operating the cluster, not only querying it. - A rigorous approach to relevance measurement: you have owned offline evaluation, golden sets, and A/B or interleaving frameworks, and you can explain what moved a metric and why. - Fluency across the recommender stack — candidate generation, feature pipelines, embedding retrieval, re-ranking — and a working understanding of the serving constraints that make an offline win fail online. - Strong distributed systems and backend fundamentals, with production experience in Go or a comparable systems language, and comfort in the data layer behind it (SQL warehouses, streaming and batch pipelines). - Demonstrated ownership of an ambiguous area: you have picked up something with no dedicated owner, decided what mattered, and can show the result. - Clear written communication and the judgment to work with Product on real tradeoffs between relevance, freshness, safety, and latency. - Preferred: experience with AI-generated or rapidly growing content catalogs, severe cold-start conditions, or ranking under trust-and-safety constraints. Compensation: The anticipated annual base salary range for this role is between $250,000 - $320,000 (USD). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   Benefits: - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!