Вакансии

-Анализ бизнес-требований и участие в планировании доработок. -Написание и поддержка автоматизированных тестов на Python (pytest) для бэкенд-сервисов.

Location: Москва Мы - команда эксплуатации Управления централизованных технологических решений. Мы занимаемся сопровождением и повышением надежности автоматизированных систем, обеспечивающих хранение и интеллектуальную обработку документов по основным бизнес-процессам Банка с использованием ML-моделей и AI-решений. Обязанности * администрирование и сопровождение автоматизированных систем (АС) банка * устранение инцидентов и отклонений в работе АС * настройка и поддержка промышленного ландшафта и стенда ПСИ * внедрение новых версий прикладного программного обеспечения * организация мониторинга АС и его актуализация в ходе эксплуатации АС * реализация требований надежности и кибербезопасности, предъявляемых к АС. **Наш техстек:** Linux, K8S/OpenShift, Docker Kafka, PostgreSQL, Ignite , Redis Jira, Confluence, BitBucket, Jenkins, Nexus, Ansible Prometheus, Zabbix, Grafana, Dynatrace Требования * опыт сопровождения и администрирования автоматизированных систем от 1 года * навык работы в командной сроке Linux, знание базового набора команд * знание принципов организации и построения баз данных * знание SQL (простые/средней сложности запросы) * опыт работы с платформами контейнеризации K8S/Openshift. * понимание основ виртуализации инфраструктуры (VMWare, KVM) * навык автоматизации рутинных задач с использованием скриптовых языков * инструментальное владение AI для анализа, генерации и автоматизации * знание принципов ITIL/ITSM (опционально). Условия * офис - Москва, 2-й Южнопортовый проезд, 12а * гибридный формат работы - 4 дня в неделю офис, 1 день удаленный (по завершению исп. срока) * ежегодный пересмотр зарплаты, годовое премирование * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи * ипотека для сотрудников (выгоднее до 7%) * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Remote - US About Backblaze Backblaze is the object storage leader in the open cloud movement, fueling customer success with cloud storage built purposefully to unlock budgets, unburden administrators, and unleash innovators. Together with our partners, we’re helping customers break free from the restrictive, overpriced legacy solutions that hold them back, and blaze forward with the full power of the open cloud in their hands. Founded in 2007, we scaled the business with less than $3 million in outside funding until 2021, when we did a traditional IPO on the Nasdaq stock exchange. Today, Backblaze generates over $136M ARR and is the leading specialized storage cloud, managing over three billion gigabytes of data storage for 500K+ customers in 175+ countries, including businesses, developers, IT professionals, and individuals. But while there is a lot to celebrate in our past, there is almost as much opportunity ahead of us. We’re seeking a Principal SRE to join our team! About the Role We are seeking a Principal Site Reliability Engineer (SRE) to help ensure the stability, scalability, and reliability of our production systems. The ideal candidate will have extensive experience collaborating in cross functional teams to manage the design, implementation and ongoing management of the Backblaze core infrastructure. You will be approved to demonstrate strong observability skills, process and service improvement and ability to leverage AI and automation to assist to mitigate issues faster and meet customer demand.. As a technical leader in Backblaze you will be expected to work across the organisation to achieve our goals. This will include working with our executives and customers. The role will focus long term strategic focus on platform stability. Key Responsibilities Service Reliability & Operations Own the availability and durability of critical services across production environments and accountability for outcomes. Ability to work independently, take ownership, and drive projects from problem discovery through resolution. Assist and advise on the design of service health metrics using SLIs, SLOs, and error budgets, and escalate issues when thresholds are at risk; partner with service owners to define/iterate SLIs/SLOs and establish error budget policies and alerting. Participate in leadership on-call rotations, incident response, and post-incident reviews to drive service improvements. Follow established ITIL/OSS processes (incident, change, problem, and capacity management). Architect and Design Design automation for common operational tasks, reducing manual intervention and toil. Collaborate with engineering on overall storage design, performance expectation against product SLAs. Define the monitoring, logging, and alerting frameworks (e.g., Prometheus, Grafana, Catchpoint, ELK), and help integrate runbooks with FireHydrant. Work with CI/CD pipelines, configuration management, and infrastructure as code tools (Terraform, Ansible, Jenkins). Write scripts (Bash, Python, Go, etc.) to improve system reliability and efficiency. Collaboration Partner with engineering, customer success, product, and operations teams to support resilient system design and operations. Work with customers to ensure stability prioritization is based around their needs and experiences in using Backblaze products. Work with vendors and service providers to troubleshoot service issues and track SLA performance. Document systems, share learnings, and help grow a reliability-minded engineering culture. Continuous Improvement Lead cross functional teams to identify gaps in observability, operations process and define overall prioritization strategy. Identify recurring issues and propose long-term improvements, and design action plans around implementing those improvements. Promote reliability-focused practices within development and operations teams. Qualifications Education & Experience Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience). 10+ years of experience in site reliability, systems engineering, or operations, experience with hyperscalers preferred. Exposure to large-scale, production-grade systems, including experience establishing operational procedures or runbooks and bringing others up to speed on them. Technical Skills Solid Linux systems administration and troubleshooting skills. Proven ability to leverage AI tools to improve platform stability and scalability. Knowledge of storage systems, performance, sharding and storage algorithms. Experience in load balancing services including IPVS. Security-minded operations (patching, hardening, identifying vulnerabilities). Familiarity with service reliability concepts — monitoring, alerting, incident response, and root cause analysis. Proficiency in at least one scripting language (Python, Bash, or Go). Experience working with containers (Kubernetes, Docker) and microservices concepts required; comfortable operating in a Kubernetes/Vitess-on-K8s environment (kubectl, mysqlsh, Vitess keyspaces). Experience with Hashicorp products (Nomad, Vault, Terraform). Preferred Attributes Experience in a SaaS, service provider, or distributed systems environment. Familiarity with ITIL/OSS practices and SLO/SLA’s. Strong problem-solving skills and willingness to learn new technologies. Experience with cloud platforms (AWS, GCP, or Azure). Backblaze Perks Healthcare for family, including dental and vision Competitive compensation and 401K RSU grants for full-time employees ESPP program Flexible vacation policy Maternity & paternity leave MacBook Pro to use for work, plus a generous stipend to personalize your workstation Childcare bonus (human children only) Fertility treatment and support Learning & development program Commuter benefits Culture that supports a healthy work-life balance Pay Transparency To provide greater transparency to candidates, we share pay ranges for all US-based job postings regardless of state. Final offer amounts are determined by multiple factors including candidate location, skills, depth of work experience, and relevant licenses/credentials. The pay range for this position is $215,000 to $250,000 per year. At Backblaze, we value being fair and good to our customers, partners, and employees. That’s why diversity, equity, and inclusion are at the core of our values. We are committed to fostering a workforce where all employees feel a sense of belonging regardless of race, ethnicity, nationality, gender, sexual orientation, age, religion, socio-economic status, ability, veteran status, and education. We believe that our dedication to cultivating a diverse workspace not only allows us to better serve our customers in over 175 countries but further reinforces our commitment to doing the right thing. We are proud to be an Equal Opportunity Employer. To understand more about the data we collect and process as part of your application, please view our Backblaze Employee Privacy Notice.

Lovable is looking for a Data Infrastructure Engineer to build and operate scalable multi-tenant data infrastructure, storage, queries, connectors, and observability.

Location: 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. 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. 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.

Location: 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 manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. 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. 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.

Location: Prague, Czechia; Lisbon, Portugal; Madrid, Spain CloudLinux is a global remote-first company. We are driven by our principles: do the right thing, employees first, we are remote first, and we deliver high-volume, low-cost Linux infrastructure and security products that help companies to increase the efficiency of their operations. Every person on our team supports each other and does what we can to ensure we all are successful. Check out our website for more information: cloudlinux.com We are currently hiring Staff Engineers for various projects within the Imunify360 product. Imunify360 is CloudLinux's multi-layer security suite for Linux web servers: antivirus and malware cleanup, WAF, IDS/IPS, Proactive Defense, patch management (KernelCare/Patchman), and IP/domain reputation. It runs on hundreds of thousands of shared, VPS and dedicated hosting servers, under cPanel, Plesk, DirectAdmin and standalone. The engineering org is 60+ engineers in squads covering backend, core, server agent, malware research, WAF/WebShield, and Patchman. Core services are Python; agents and performance-critical paths are Go and Rust. We run on Hetzner and our own bare metal. Check out our website for more information: https://imunify360.com/ Role Mission & Scope Most complex technical challenges in Imunify360 go beyond individual squads. A single detection change can impact the scanner, agent, UI, reputation backend, and release process. While squad leads own delivery within their teams, the Staff Engineer ensures technical alignment across the product. As a Staff Engineer, you'll be a hands-on technical leader working across the entire product , focusing on the most critical and high-impact challenges as priorities evolve. You'll drive technical decisions, improve architecture, establish engineering standards, and help teams build sustainable solutions. This is an individual contributor role , without direct people management responsibilities. You'll remain actively involved in development, spending approximately 30–50% of your time writing production code , rather than focusing solely on architectural design. While deep expertise in a particular area is valued, this role requires the flexibility to work across different components, collaborate with multiple squads, and tackle challenges wherever your expertise can make the greatest impact. What you will own Architecture and technical direction Own the end-to-end architecture of Imunify360 as a product: agent ↔ server ↔ cloud backends, data flows, and the contracts between them. Write and drive ADRs and RFCs for cross-squad changes. Run the Wed/Fri architecture sync together with the Technical Director. Keep a living tech-debt and risk register (security / compliance / uptime) with owners and dates, and get the top items funded on the roadmap. Hard cross-cutting problems (examples of what lands on your desk) Agent resource footprint on dense shared hosts: CPU, memory and I/O budgets for scanning, file monitoring (inotify/fanotify) and WAF, measured on real kernels, not benchmarks from elsewhere. Detection pipeline quality: false positive / false negative rates, signature and ML model rollout with staged gates and rollback. Upgrade and release safety across a large, heterogeneous fleet (CentOS/AlmaLinux/CloudLinux/Ubuntu, multiple control panels, old kernels). Backend scale and cost of reputation, ThreatIntel and telemetry services. Migrating hot paths from Python to Go/Rust where the data shows it pays off, and saying no where it doesn't. Engineering standards Set the bar for design reviews, code review, testing strategy, observability (Prometheus/Grafana/Sentry) and secure SDLC (OWASP ASVS, CIS Benchmarks as relevant). Lead or co-lead SEV1/SEV2 incidents when the cause is unclear or cross-squad; make sure the blameless post-mortem ships within 24 h and its actions get closed. Drive the AI-augmented SDLC in practice: show what good agentic coding looks like (prompt specificity, context provision, review quality, tool boundaries) on real changes, not slides. People multiplier (no direct reports) Mentor senior engineers and squad leads toward staff-level scope. Act as the technical bar-raiser in hiring loops for senior and lead roles. Be the engineering voice in product and sales discussions: estimates, feasibility, and no dated customer commitment without a written squad-lead estimate. Key Milestones 30 days: Shipped at least one non-trivial change to production. Can explain the full request path from agent to backend. Has a personal list of top 10 technical risks. 90 days: Owns one cross-squad initiative with an approved ADR, a measured baseline, and a staged rollout plan. Published risk register reviewed in arch sync. 6 months: That initiative is in production with before/after metrics. At least one engineering standard (e.g. release gating, agent perf budget, design review template) is adopted by 3+ squads. 12 months: Recognized by squad leads as the default escalation point for cross-cutting technical decisions. At least one senior engineer grown into broader scope with your mentorship. Requirements Must have 10+ years building and operating production software, with 3+ years in a Staff/Principal/Tech Lead scope spanning several teams. Strong production experience in Python and at least one of Go or Rust . Deep Linux internals : processes, filesystems, networking, kernel interfaces (inotify/fanotify, eBPF or similar), packaging (RPM/DEB), systemd. Track record of designing and running distributed systems at scale: async backends, queues, databases, caching, failure modes. You have owned systems that run on customer machines (agents, daemons, on-prem software) and know what upgrade safety, backward compatibility and resource budgets mean there. Security mindset: threat modeling, secure coding, handling of untrusted input (files, HTTP traffic, user content). Writes clear, short technical documents and drives decisions in async, remote teams across time zones. Uses AI coding tools (Cursor, Claude, MCP-based tooling) daily and can judge their output critically. Nice to have Experience in server security: AV/malware detection, WAF (ModSecurity/Coraza), IDS/IPS, rootkit detection, CVE/patch management. Experience with WordPress and Node.JS web application hosting at scale ML in production for classification/detection, including evaluation and safe model rollout. Observability at scale: Prometheus, Grafana, Sentry, ClickHouse or similar. CI/CD ownership: GitLab CI, Jenkins, build systems for multi-distro packaging. Open-source contributions in security or systems tooling. Benefits What's in it for you? Stay hands-on technically while owning complex, high-impact engineering work. Work on challenging Linux, automation, and cybersecurity problems across mature and newer products. Influence technical direction and make meaningful architectural decisions. Work independently with a high level of ownership and autonomy. Fully remote work with flexible working hours. 24 paid vacation days, 10 national holidays, and unlimited sick leave. Private medical insurance compensation. Co-working and gym/sports reimbursement. Education budget. Opportunity to receive a reward for innovative ideas that the company can patent. By applying for this position, you consent to the processing of your personal data as described in our Privacy Policy ( https://cloudlinux.com/candidate-privacy-notice ), which provides detailed information on how we maintain and handle your data.

проектирование, разработка и поддержка высоконагруженных backend-систем; сопровождение и мониторинг работы сервисов в продуктивной среде;

Проводить ручное тестирование веб-платформы по выявлению и управлению уязвимостями в кибер-безопасности (фронтенд + бэкенд); Тестировать новый функционал и проводить регрессионное тестирование продукта;

Ручное тестирование frontend-backend архитектуры; Создание и поддержание документации по тестированию, тест-кейсов и чек-листов;

Задачи: — функциональное тестирование backend сервисов и UI и написание и поддержка тестовой документации (чек-листы, тест-кейсы, баг-репорты, инструкции) — подготовка тестовых данных для тестирования / проведения ПСИ, участие в релизах, проведение регрессионного тестирования и в проведении сценариев приемо-сдаточных испытаний (ПСИ) — участие в работе с обращениями от пользователей и анализ информации в БД и логах сервисов — построение и поддержка eval-набора (golden set) и регрессионного контроля качества при смене моделей, промптов и версий приложения — автоматизированная оценка качества генерации (LLM-as-judge, метрики типа Ragas) при релизах — тестирование RAG: качество поиска релевантного контекста, корректность источников, поведение при неполной / противоречивой информации — тестирование агентных сценариев: многошаговые цепочки, вызовы инструментов (MCP), повторные попытки, эскалация к человеку — участие в мониторинге качества в проде и настройке обратной связи от пользователей. Требования: — опыт работы в тестировании от 3 лет — понимание HTTP, кодов ответов, авторизации — опыт тестирования API (REST/JSON) для проверки интеграций и бизнес-логики, микросервисного взаимодействия — опыт тестирования запросов с помощью Postman/Insomnia/Swagger — опыт работы с форматами данных: JSON, YAML, XML, HTML — знание SQL, умение написать SQL запрос средней сложности — базовый опыт работы с системами сбора и визуализации метрик, работа с логами (Prometheus, Grafana) и понимание CI/CD и участия QA в пайплайнах доставки — понимание принципов работы LLM и их ограничений: недетерминированность вывода, галлюцинации, методы оценки качества генеративных моделей и опыт построения eval-наборов (golden set) и оценки качества LLM — желательно — LLM-as-judge, Ragas. Будет плюсом: — понимание RAG и методов оценки качества retrieval: релевантность контекста, корректность источников — опыт тестирования агентных систем: многошаговые агенты, tool calling — опыт работы с Python для автоматизации оценки (pytest) — понимание метрик качества AI-продукта и мониторинга качества в проде — опыт работы с Kafka — опыт работы с векторными / графовыми БД (например, Qdrant) Условия: — гибридный формат работы из офиса в г. — Москва — годовой бонус и ежегодный пересмотр — ДМС с первого дня + стоматологию и льготное страхование для семьи — корпоративный университет Сбера, внутреннюю образовательную платформу, участие в IT-конференциях — офис на Ленинском проспекте с зонами отдыха и спортзалом — льготную ипотеку в Сбере, корпоративную пенсионную программу, подписку СберПрайм с возможностью совместного использования на трёх близких, скидки от партнеров и сервисов группы компаний.

Тестированием Frontend и Backend частей облачных сервисов в рамках задач продуктовых команд; Автоматизацией процесса тестирования;

Разрабатывать и поддерживать CI-пайплайны: сборку компонентов, запуск автоматических проверок, публикацию артефактов и подготовку тестовых окружений; Автоматизировать создание, настройку, обновление и удаление стендов разработки и тестирования;

Создавать и поддерживать IaaS для множества собственных проектов компании на собственных ресурсах и арендуемых сервисах. Работать над повышением стабильности и отказоустойчивости инфраструктуры и инфраструктурных сервисов.

Стандартизации эксплуатационных и DevOps-практик, включая разработку типовых шаблонов CI/CD, Helm-чартов, конфигураций мониторинга, логирования и правил эксплуатации; Развития систем мониторинга и наблюдаемости (observability);

Задачи: — Разворачивание новых, поддержка и развитие существующих приложений в Kubernetes; — Создание пайплайнов CI/CD, поддержка текущих; — Взаимодействие с командами разработки и тестирования; — Администрирование кластеров Kubernetes с приложениями в различных средах разработки (dev, test) и продуктовом облаке. Требования: — Опыт администрирования ОС Linux (Centos/Ubuntu/Debian) от 5 лет; — Опыт администрирования и использования систем управления контейнерами - Kubernetes, OpenShift (у нас k8s) от 2 лет; — Опыт работы с CI/CD - инструментами и настройкой процессов сборки и доставки кода приложений (у нас Gitlab CI); — Опыт работы с Docker, опыт сборки образов; — Опыт работы с Helm, Ansible; — Опыт администрирования и использования инструментов мониторинга (Prometheus, Grafana); — Понимание принципов работы микросервисных платформ и их компонентов. Будет плюсом: — Опыт работы с высоконагруженными микросервисами; — Знание Python/GoLang. Условия: — Реализация проектов федерального масштаба (облако и сеть облачных дата-центров, которым нет аналогов в России) в составе крупного международного промышленного холдинга; — Оформление по ТК РФ, белая зарплата, уровень вознаграждения обсуждается с успешным кандидатом; — Современный офис в 10–15 минутах пешком от м. «Улица 1905 года», на территории бизнес-кластера «Трехгорная мануфактура»; — Гибридный график работы; — Льготы для аккредитованных ИТ-компаний; — ДМС после испытательного срока, в том числе для родственников; — Скидки от компаний-партнеров (BestBenefits, Ингосстрах); — Компенсация спорта и заботы о здоровье (Фитмост); — Корпоративный университет, библиотека Alpina Digital; — Корпоративное питание; — Мобильная связь; — Курсы английского языка; — Программа развития персонала по итогам Performance review; — Материальная помощь в важных жизненных событиях; — Отсутствие бюрократии, быстрое принятие решений.

Чем предстоит заниматься: Проектирование и разработка новых микросервисов и бизнес-логики. Оптимизация производительности существующих компонентов системы, работа с высоконагруженными участками кода.

Location: San Francisco, CA 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 manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. 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. 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.