Вакансии

Обязанности: Запуск автотестов. Участие в разработке фреймворка и утилит для тестирования программно-аппаратных комплексов.

— Проводить ручное тестирование Web и Mobile приложений (преимущественно Web); — Анализировать требования, проверять новую функциональность и пользовательские сценарии;

• Опыт работы Manual QA от 1 года.

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

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

QA работает в тесной связке с developers и product team и участвует во всем цикле разработки — от подготовки тест-кейсов до automation, regression testing и проверки готовности функционала к release. • 2+ года коммерческого опыта в QA.

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

Location: United States (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. The Opportunity: Grafana Labs is seeking a Senior Partner Solutions Engineer to join its high growth startup environment and help scale cloud partnerships, with a dedicated focus on joint go to market activities with Cloud Service Providers (CSP) including Amazon Web Services (AWS) and Google Cloud (GCP). The successful candidate will be instrumental in enabling partner success through education, enablement, direct engagement, support of events, and build content that helps our CSP’s position and demonstrate Grafana capabilities. What You’ll Be Doing: Partnering with Channel Sales and Field Sales to convey Grafana’s vision, value, and technical strategy to cloud partners Leading technical engagement with partners to drive deal progression and close opportunities Delivering product demos, technical presentations, and hands-on enablement sessions Develop and deliver training, onboarding, and ongoing education for solutions architects, and technical teams Represent Grafana Labs through technical presentations, live demos, webinars, conference sessions, and regional partner events Create reference architectures, demo environments, solution guides, and presentations that explain the value of Grafana Cloud alongside cloud provider services. Track partner readiness, reuse of technical assets, and progress of joint opportunities, and bring partner insights back to product and GTM teams Shaping and expanding the reach of Grafana Partner University across multiple partner personas Collaborating with internal teams to provide product and competitive feedback Support selling motions like Sell-To, Embedded, and PoweredBy, either directly or jointly with partners Run high impact workshops and 1:1 training to generate and progress pipeline Travelling up to 50% to support cloud partner events, workshops, and field activities What Makes You a Great Fit: Located: United States (Remote) 6+ years in pre-sales or solution architecture, ideally within the observability space Strong experience selling SaaS software across the NORAM region Proven use of MEDDIC or similar qualification frameworks Excellent communication and interpersonal skills, especially in remote, global teams Demonstrated ability to operate in a fast-paced, ambiguous, startup environment Strong judgement, leadership, and problem-solving abilities Passion for education, enablement, and helping others succeed technically Bonus Points For: Experience with CSP’s and a strong understanding of their core infrastructure services. Experience with cloud marketplaces, co-selling, or joint solution launches. Solid understanding of observability and cloud native tooling Background in OSS first companies Experience with Grafana, Prometheus, and Loki Hands-on experience deploying and operating Kubernetes Bachelor’s degree in CS, IT, Math, or related field (or equivalent experience) Compensation & Rewards: In the United States, the OTE (On-Target Earnings) compensation range for this role is $204,000 - $244,000 . Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

— Тестировать веб-приложения вручную (UI и API) и с помощью автотестов. — Планировать тестирование, находить и описывать баги.

- Проектировать и разрабатывать компоненты programmatic-платформы; - Проектировать отказоустойчивые, масштабируемые, поддерживаемые сервисы;

Location: Colombia; Brazil; Argentina Solvd Inc. is a rapidly growing AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. We work with industry-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes. Following the acquisition of Tooploox, a premier AI and product development company, Solvd now offers true end-to-end delivery—from strategic advisory and solution design to custom AI development and enterprise-scale implementation. Our capability centers combine deep technical expertise, proven delivery methodologies, and sector-specific knowledge to address complex business challenges quickly and effectively. Solvd is hiring a Security Engineer to help design and secure a modern, cloud-first environment built around identity, automation, and Zero Trust. In this role, you will own security across endpoints, SaaS platforms, and access controls, using scripting and AI to build scalable, efficient security operations for a distributed workforce. What You’ll Do - Architect the Modern Perimeter: Lead security strategy for a 100% remote/hybrid workforce where Identity is the new perimeter. - Automation & Scripting: Take full ownership of our Conditional Access policies and security workflows. You will be expected to write and maintain scripts to automate onboarding, offboarding, and real-time posture enforcement. - AI as a Force Multiplier: Proactively use AI and LLMs to enhance your technical output, automate repetitive tasks, and solve complex security puzzles faster than traditional methods. - Logical Problem Solving: Approach security from a first-principles perspective. Since we don't have a traditional stack, you must be able to think logically to build guardrails that don't exist yet. - Endpoint & SaaS Security: Oversee device management (macOS/Windows) and the security configuration of our SaaS ecosystem (Rippling, Office 365, Salesforce). - Zero Trust & Identity: Implement and refine Zero Trust access, DLP policies, and SSO enforcement across all vendor platforms. - Compliance & Governance: Ensure our cloud-native operations remain aligned with SOC2, HIPAA, and GDPR requirements. Who You Are - A Modern Security Engineer: You understand that security today is about protecting data and identity, not firewalls and servers. - Logic-Driven: You enjoy breaking down complex problems and building elegant, logical solutions. - Scripting Savvy: You are highly proficient in scripting (Python, PowerShell, or Bash) and see manual tasks as a failure of automation. - AI-Forward: You are already using AI tools to help you code, document, and troubleshoot. You know how to use these tools to do the work of a much larger team. - Independent: With no internal apps or physical infra to manage, you are comfortable owning the entire IT security lifecycle end-to-end. Key Technologies You’ll Work With - Zero Trust Architecture - Conditional Access Policies - Modern Identity - SaaS Operations - AI Tools - MDM When you join Solvd, you'll… - Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation. - Be part of a global team with equal opportunities for collaboration across continents and cultures. - Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards. Ready to make an impact? If you're excited to build things that matter, champion responsible AI, and grow with some of the industry’s sharpest minds. Apply today and let’s innovate together. Solvd is an equal opportunity employer.

• Build high-performance edge services in Go. • Design offline-first and cloud-to-edge architecture.

— Разработка baсkend реализаций для веб-сервисов. — Участие в разработке архитектуры и реализации прототипов новых веб-сервисов.

Обязанности: • Проведение функционального, регрессионного, интеграционного и приёмочного (UAT) тестирования мобильных (iOS/Android) и веб-приложений на всех этапах разработки и релиза. Требования: • Опыт работы в QA — от 3 лет. • Практический опыт в Mobile и/или API Test Automation. • Достаточные технические знания...

• От 5 лет опыта в Data Science / ML Engineering, из них от 2 лет в управлении DS/ML-командой. • Обязательный production-опыт с recommendation systems (retrieval, ranking, personalization).

🔹 Design and build API endpoints used by customers. 🔹 Integrate the platform with third-party services.

Location: United Kingdom; Brazil; Ireland; Peru; Costa Rica; Latvia; Argentina; Romania; Cyprus; Portugal; Slovenia; South Africa; Czech Republic; Panama; Estonia; Canada; Hungary; Mexico; Poland; Spain; Bulgaria Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients. The AI Infrastructure team builds and operates the production systems that power intelligent agents at scale. This team sits at the foundation of the agent platform, ensuring that model inference, orchestration, and execution layers are reliable, observable, and performant under real-world load. Working closely with the Agent Systems team and broader infrastructure partners, this group owns the core primitives that enable agents to safely operate across internal systems. The environment is high-scale and high-stakes — systems serve millions of users and must meet strict reliability, latency, and correctness standards. This is a deeply production-oriented team. Engineers here combine strong systems thinking with applied ML infrastructure experience, building in Rust and operating services where performance and failure modes matter.   THE OPPORTUNITY - Design and build the infrastructure layer powering AI agent systems in production - Develop high-performance Rust services that handle model inference, orchestration, and execution - Architect scalable systems capable of supporting millions of users and high request throughput - Build reliable ML infrastructure and MLOps patterns for model deployment, evaluation, and monitoring - Define guardrails, observability, and failure handling for agent-driven workflows - Optimize latency, throughput, and cost across inference and orchestration layers - Partner closely with the Agent Systems team to translate experimental prototypes into hardened production systems - Contribute to foundational infrastructure decisions in a high-scale, high-impact environment   WHAT YOU BRING - 5+ years of experience building and operating high-scale production systems - Strong proficiency in Rust and systems-level programming - Deep understanding of distributed systems, reliability engineering, and performance optimization - Experience operating services serving millions of users or high-throughput workloads - Familiarity with ML infrastructure, model serving, or MLOps in production environments - Experience designing observability, monitoring, and failure recovery systems - Strong collaboration skills working across infrastructure and applied engineering teams - High ownership mindset in high-stakes production environment   NICE TO HAVES - Experience building infrastructure for agent-based or LLM-powered systems - Background in high-performance networking, async systems, or low-latency architectures - Experience with container orchestration and cloud-native infrastructure - Familiarity with evaluation frameworks and model performance monitoring at scale - Experience working in fast-moving 0→1 or platform-building teams Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

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