Вакансии

Location: San Francisco Perplexity is continuing to rapidly scale our Enterprise Engineering Organization and seeking Engineering Managers with deep expertise across the Enterprise domain to help lead the next wave of growth. As an Enterprise Engineering Manager, you will combine technical judgment, product intuition, and people leadership to help large organizations adopt AI at scale. We are hiring across the enterprise control plane, enterprise data plane, and connector marketplace and activation. Depending on your area of focus, you will lead and grow a team in one of these areas, addressing challenges such as: Enterprise Control Plane: Building the identity, security, and governance foundations that enable large organizations to deploy Perplexity confidently, including SSO, SCIM, role-based access, customer-managed encryption keys (BYOK), admin APIs, and enforceable policies governing access to data, models, and tools. Supporting regulated-industry requirements, including SOC 2, HIPAA, data governance, and residency, with audit logs and analytics that give administrators visibility into how Perplexity is used. Enterprise Data Plane: Building the connector platform and semantic data layer that give Computer reliable, permission-aware access to enterprise systems and knowledge, including managed authorization, private-system access, and data freshness. Connector Marketplace and Activation: Building discovery, recommendations, onboarding, and marketplace experiences that help customers find and adopt integrations, while working with partners to expand the connector ecosystem. Across these areas, you will translate enterprise customer needs into scalable technical solutions across Perplexity’s products. RESPONSIBILITIES - Set technical direction for your team and guide architecture decisions across the enterprise platform. - Build the foundational systems that enable large organizations to deploy Perplexity broadly and confidently. - Shape roadmaps with product and design, applying strong technical judgment and product intuition. - Set a high bar for reliability, code quality, testing, and maintenance. Lead incident response and ensure platform changes and connector migrations are rolled out safely. - Partner with engineering teams across the company to ensure the platforms we build are extensible and easy to adopt. - Represent engineering in strategic enterprise customer conversations, partnering with sales and customer success to understand requirements and remove barriers to adoption. - Oversee hiring, onboarding, and mentorship for a rapidly growing team. Develop rigorous interview pipelines and work closely with recruiting to source candidates. - Work with Perplexity’s co-founders to deliver enterprise engineering priorities. QUALIFICATIONS - Strong technical judgment and experience building enterprise platforms or products, with an understanding of the reliability, security, and usability requirements of enterprise customers. - Domain expertise in at least one of the following areas: - Enterprise control plane: identity and access management, SSO/SCIM, role-based access, key management, admin APIs, policy enforcement, audit logging, data governance, or compliance and residency requirements for regulated industries. - Enterprise data plane: connector and integration platforms, including authentication, source-system permissions, data synchronization, and reliability at scale; or semantic data layers and knowledge platforms that make enterprise data accessible and useful to AI. - Connector marketplace and activation: discovery and recommendation systems, integration onboarding, marketplace product UX, or technology partner ecosystems. - Ability to deliver in a fast-moving environment while balancing customer needs, technical quality, and long-term maintainability. - Detail-oriented and quality-focused - our customers run critical workflows on Perplexity, and our product needs to stay robust and reliable. - Ability to translate enterprise customer needs into clear technical priorities and communicate tradeoffs effectively. - Experience building and managing high-performing engineering teams, with a strong management practice grounded in clear feedback, effective 1:1s, career development, and recruiting and retaining high-caliber talent. - Ability to lead parallel technical initiatives, align strong technical perspectives, and make clear prioritization and tradeoff decisions. - Experience with AI/ML products, enterprise AI adoption, or scaling enterprise platforms at a startup is a plus. - 8+ years of engineering experience, with at least 4 of those years as an engineering manager.

San Francisco

Perplexity43 дн назад

Nebius44 дн назад

Location: Independence, Missouri, United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are looking for a Senior Hardware Support Engineer to own production hardware reliability across large-scale, mission-critical data center environments. This role operates at the intersection of hardware engineering, operations, and vendors, ensuring fleet stability, rapid root cause identification, and continuous improvement of server and platform reliability. You will act as a senior escalation point for complex hardware and firmware issues affecting production systems, driving investigations from symptom to root cause and coordinating resolution across engineering, vendors, and on-site teams. The role requires strong analytical thinking, structured problem-solving methodology, and deep hardware expertise in high-density, performance-critical infrastructure environments. Your responsibilities will include Leading root cause analysis for complex hardware and firmware failures across production fleets Aggregating recurring problems and error patterns to identify systemic reliability issues Acting as the senior escalation point for hardware-related incidents impacting availability or performance Coordinating with vendors to drive timely diagnostics, RMAs, firmware fixes, and corrective actions Partnering with internal engineering teams to validate fixes and prevent recurrence Performing hardware and firmware validation before fleet-wide rollout Driving structured incident investigations using established IT problem management methodologies Supporting on-site teams with technical coordination during critical hardware events Improving hardware observability, failure tracking, and reporting processes Contributing to long-term hardware reliability strategy and fleet-wide stability improvements What we expect you to have Strong hands-on expertise with server hardware in data center or large-scale production environments Proven experience performing root cause analysis of hardware and firmware failures Deep understanding of server components (CPU, memory, storage, networking, power, BMC) and failure modes Experience working directly with hardware vendors and engineering teams to resolve production issues Structured problem-solving skills using formal IT or incident management methodologies Strong analytical capabilities and ability to interpret logs, telemetry, and error patterns Experience coordinating technical activities with on-site operations teams Ability to manage multiple concurrent investigations with production impact Clear written and verbal communication skills in cross-functional environments It will be an added bonus if you have Experience in GPU-dense, AI, or high-performance computing environments Exposure to firmware lifecycle management and large-scale rollout validation Familiarity with Linux-based production systems and infrastructure tooling Experience improving fleet-wide hardware reliability metrics at scale Working conditions Onsite work in the Independence Missouri Data center. Participation in incident escalations for production-impacting events Key employee benefits Comprehensive medical, dental, and vision coverage 401(k) plan with company contribution Flexible paid time off Paid parental leave Professional development support Compensation $125,000 – $180,000 per year plus annual performance-based bonus. #LI-AL1 Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $147,200 — $183,900 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Nebius44 дн назад

Senior Radio Frequency Engineer

Location: Birmingham, Alabama, United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are looking for a Senior Hardware Support Engineer to own production hardware reliability across large-scale, mission-critical data center environments. This role operates at the intersection of hardware engineering, operations, and vendors, ensuring fleet stability, rapid root cause identification, and continuous improvement of server and platform reliability. You will act as a senior escalation point for complex hardware and firmware issues affecting production systems, driving investigations from symptom to root cause and coordinating resolution across engineering, vendors, and on-site teams. The role requires strong analytical thinking, structured problem-solving methodology, and deep hardware expertise in high-density, performance-critical infrastructure environments. Your responsibilities will include Leading root cause analysis for complex hardware and firmware failures across production fleets Aggregating recurring problems and error patterns to identify systemic reliability issues Acting as the senior escalation point for hardware-related incidents impacting availability or performance Coordinating with vendors to drive timely diagnostics, RMAs, firmware fixes, and corrective actions Partnering with internal engineering teams to validate fixes and prevent recurrence Performing hardware and firmware validation before fleet-wide rollout Driving structured incident investigations using established IT problem management methodologies Supporting on-site teams with technical coordination during critical hardware events Improving hardware observability, failure tracking, and reporting processes Contributing to long-term hardware reliability strategy and fleet-wide stability improvements What we expect you to have Strong hands-on expertise with server hardware in data center or large-scale production environments Proven experience performing root cause analysis of hardware and firmware failures Deep understanding of server components (CPU, memory, storage, networking, power, BMC) and failure modes Experience working directly with hardware vendors and engineering teams to resolve production issues Structured problem-solving skills using formal IT or incident management methodologies Strong analytical capabilities and ability to interpret logs, telemetry, and error patterns Experience coordinating technical activities with on-site operations teams Ability to manage multiple concurrent investigations with production impact Clear written and verbal communication skills in cross-functional environments It will be an added bonus if you have Experience in GPU-dense, AI, or high-performance computing environments Exposure to firmware lifecycle management and large-scale rollout validation Familiarity with Linux-based production systems and infrastructure tooling Experience improving fleet-wide hardware reliability metrics at scale Working conditions Onsite work in the Birmingham Alabama Data center. Participation in incident escalations for production-impacting events Key employee benefits Comprehensive medical, dental, and vision coverage 401(k) plan with company contribution Flexible paid time off Paid parental leave Professional development support Compensation $125,000 – $180,000 per year plus annual performance-based bonus. #LI-AL1 Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $147,200 — $183,900 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Whoop44 дн назад

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. As a Senior RF Engineer on the WHOOP Hardware team, you will support the development, integration, validation, and delivery of the wireless systems that power WHOOP's wearable products. Working closely with Electrical, Firmware, Mechanical, Manufacturing, and Product teams, you will independently drive RF development and validation activities from early prototypes through production. WHOOP products rely on highly integrated wireless systems operating within compact and challenging wearable form factors. You will bring strong hands-on RF test and debugging experience to characterize system performance, identify root causes of wireless issues, develop robust validation methodologies, and ensure designs meet performance, regulatory, and manufacturing requirements. RESPONSIBILITIES - Own RF system characterization, validation, and debugging from early prototypes through production. - Define validation plans, test methods, and acceptance criteria for conducted, radiated, and system-level wireless performance. - Characterize transmitter, receiver, antenna, coexistence, interference, desense, and OTA performance, including return loss, antenna efficiency, TRP/TIS, and on-body performance as applicable. - Develop repeatable test setups, fixtures, scripts, and automated measurement methods to improve validation coverage, efficiency, and data quality. - Analyze RF performance data, identify failure modes and root causes, and drive corrective actions with Electrical, Firmware, Mechanical, and Manufacturing teams. - Guide RF-critical schematic and PCB design, including matching networks, grounding, shielding, routing, component selection, and antenna integration. - Apply RF circuit, electromagnetic, and system analysis using tools such as CST, HFSS, MATLAB, Python, or similar tools. - Establish production test methods, limits, and correlation studies while supporting manufacturing troubleshooting and wireless regulatory certification. - Advance next-generation RF architectures, antenna technologies, validation methods, and manufacturing techniques while communicating technical risks and recommendations across engineering teams. - Travel up to 15%, domestically and internationally. QUALIFICATIONS - BS or higher in Electrical Engineering or related field. - 5+ years of relevant RF or wireless engineering experience. - Demonstrated expertise in RF system characterization, validation, debugging, and root-cause analysis. - Background in conducted and radiated wireless testing, including transmitter performance, receiver sensitivity, antenna performance, coexistence, and desense. - Hands-on proficiency with RF lab equipment, including spectrum analyzers, signal generators, VNAs, oscilloscopes, wireless communication testers, and calibration tools. - Familiarity with antenna and OTA measurements, including return loss, efficiency, radiation performance, TRP, and TIS. - Strong command of RF and electrical engineering principles, including impedance matching, transmission lines, circuit design, PCB layout, grounding, shielding, and antenna integration. - Working knowledge of RF test automation, data analysis, or electromagnetic simulation using tools such as Python, MATLAB, LabVIEW, CST, or HFSS. - Excellent communication skills, with experience supporting product validation, manufacturing, and wireless regulatory certification. - Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibilityThe WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values. At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success. The U.S. base salary range for this full-time position is $140,000 - $195,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

Nebius44 дн назад

VoIP Engineer / Asterisk Engineer

Location: New Jersey, United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are looking for a Senior Hardware Support Engineer to own production hardware reliability across large-scale, mission-critical data center environments. This role operates at the intersection of hardware engineering, operations, and vendors, ensuring fleet stability, rapid root cause identification, and continuous improvement of server and platform reliability. You will act as a senior escalation point for complex hardware and firmware issues affecting production systems, driving investigations from symptom to root cause and coordinating resolution across engineering, vendors, and on-site teams. The role requires strong analytical thinking, structured problem-solving methodology, and deep hardware expertise in high-density, performance-critical infrastructure environments. Your responsibilities will include Leading root cause analysis for complex hardware and firmware failures across production fleets Aggregating recurring problems and error patterns to identify systemic reliability issues Acting as the senior escalation point for hardware-related incidents impacting availability or performance Coordinating with vendors to drive timely diagnostics, RMAs, firmware fixes, and corrective actions Partnering with internal engineering teams to validate fixes and prevent recurrence Performing hardware and firmware validation before fleet-wide rollout Driving structured incident investigations using established IT problem management methodologies Supporting on-site teams with technical coordination during critical hardware events Improving hardware observability, failure tracking, and reporting processes Contributing to long-term hardware reliability strategy and fleet-wide stability improvements What we expect you to have Strong hands-on expertise with server hardware in data center or large-scale production environments Proven experience performing root cause analysis of hardware and firmware failures Deep understanding of server components (CPU, memory, storage, networking, power, BMC) and failure modes Experience working directly with hardware vendors and engineering teams to resolve production issues Structured problem-solving skills using formal IT or incident management methodologies Strong analytical capabilities and ability to interpret logs, telemetry, and error patterns Experience coordinating technical activities with on-site operations teams Ability to manage multiple concurrent investigations with production impact Clear written and verbal communication skills in cross-functional environments It will be an added bonus if you have Experience in GPU-dense, AI, or high-performance computing environments Exposure to firmware lifecycle management and large-scale rollout validation Familiarity with Linux-based production systems and infrastructure tooling Experience improving fleet-wide hardware reliability metrics at scale Working conditions Onsite work in the Vineland New Jersey Data center. Participation in incident escalations for production-impacting events Key employee benefits Comprehensive medical, dental, and vision coverage 401(k) plan with company contribution Flexible paid time off Paid parental leave Professional development support Compensation $125,000 – $180,000 per year plus annual performance-based bonus. #LI-AL1 Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $147,200 — $183,900 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Компания не указана44 дн назад

Задачи: — Администрировать и поддерживать телефонию на базе Asterisk — Настраивать dialplan, SIP trunks, endpoints и маршрутизацию звонков — Подключать и управлять SIP-провайдерами — Поддерживать входящие и исходящие звонки — Интегрировать Asterisk с backend-системами через ARI / AMI и CRM — Диагностировать SIP/RTP-трафик и проблемы с качеством связи — Работать с Asterisk Realtime и MariaDB/MySQL — Управлять CDR и записями звонков — Мониторить инфраструктуру и самостоятельно устранять инциденты Требования: — 2+ года практического опыта с Asterisk — Уверенное знание SIP, VoIP, NAT, RTP, codecs — Практический опыт работы с ARI и/или AMI — Уверенное администрирование Linux — Опыт работы с MariaDB/MySQL и базовые знания SQL — Опыт с firewall, fail2ban и VoIP security — Умение самостоятельно диагностировать и решать проблемы телефонии — Будет плюсом: Redis, Nginx, monitoring tools Условия: — Полностью удалённая работа — $3000 fixed / month — График: Пн–Пт, 09:00–18:00 (UTC+3) — 1 час на обед + 3 перерыва по 15 минут — Формат работы — онлайн-офис

УдалённоГрафик: Пн–Пт, 09:00–18:00 (UTC+3)

GlobalFoundries44 дн назад

Tapeout Engineer

Требования: — Высшее образование (бакалавр, BE/B.Tech) со специализацией в области электротехники (VLSI, микроэлектроника и смежные направления) — Опыт работы от 0 до 2 лет

СтажёрГеография найма не уточнена

Компания не указана44 дн назад

ML Engineer (Инженер по машинному обучению)

Задачи: — участие в проекте по обучению AI-моделей; — разработка и обучение моделей, системы инференса, численные вычисления, оптимизация производительности; — программирование на Python; — создание, решение, проверка и валидация сложных задач в области ML Engineering; — реализация или модификация ML-модели; — создание воспроизводимого процесса обучения или инференса; — оптимизация использования памяти или производительности; — поиск и исправление численных или системных ошибок; — проверка соответствия реализации требованиям по корректности и производительности. Требования: — опыт использования coding agents с Python в рабочем процессе; — степень магистра или PhD; — уровень английского языка C1. Условия: — 15 часов в неделю; — полностью удалённая работа (Global); — гибкий график, специалист самостоятельно выбирает дни и часы работы, включая выходные при желании; — оплата $80–$150 в час.

УдалённоГеография найма не уточнена

Gravie44 дн назад

Senior Platform Engineer

Location: Remote Workplace: Remote Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way. ROLE SUMMARY Gravie is seeking a Senior Platform Engineer to build and operate the platform our engineering teams ship on. AWS should be second nature: deep enough to reach for it before documentation, and you should be as comfortable working alongside AI agents as in a terminal. The ideal candidate has genuinely operational AWS expertise, has worked in or closely with a platform team before, and knows a platform only succeeds when engineers choose it. They will own shared infrastructure, pipelines, and tooling end to end, partner with our architecture team on turning direction into paved paths teams actually adopt, and treat the engineers who use the platform as the customers they are. At Gravie, engineers work closely with Product and own outcomes end to end, and we practice agentic development to increase the scope and leverage of that ownership: engineers use AI agents to help develop specs and plans, then to execute substantial multi-step engineering work, while setting guardrails and acceptance criteria and owning the quality of everything that ships. As agents take on more of the execution, the bar on problem framing, architectural judgment, system thinking, and risk identification gets higher, not lower. HOW OUR PLATFORM TEAM WORKS If you have worked on a platform team, this will be familiar. If you have not, read it carefully, because it is the job. - The platform is a product and engineers are its users. Adoption is the measure of success, not tickets closed. If teams route around something we built, that is our problem to solve, not their failure to comply. - Self-service over gatekeeping. If the answer to a common request is a ticket to us, that is a bug in the platform. - We pave roads, we do not police them. Standards belong in pipelines, templates, and defaults, not in reminders and review comments. - Small team, wide surface. Leverage matters more than heroics, which is why AI tooling and automation are not side projects here - they are how the surface stays covered. - We operate what we ship. On-call for platform services is ours. So is the postmortem, and so is fixing the class of problem rather than the instance. - Documentation is a deliverable. A capability nobody can find or adopt without asking us is not finished. KEY RESPONSIBILITIES - Build and operate the shared platform: CI/CD pipelines, service templates, deployment tooling, and the shared infrastructure services teams depend on. - Own AWS infrastructure as code across multiple accounts, in Terraform, CDK, or both, held to the same standard as application code, including review, testing, and a real rollback story. - Implement and sustain the paved paths teams build on, partnering with architecture on direction while owning the operational reality once a path is live. - Operate what you build: on-call for platform services, incident response, the postmortem, and fixing the class of problem rather than the instance. - Build observability in by default: metrics, logs, traces, SLOs, and alerts that page a human only when a human is actually needed. - Own platform security posture alongside Security: identity and permission boundaries, secrets handling, network and data isolation, and remediation of findings in a HIPAA and SOC 2 environment. - Make cloud spend attributable and defensible through tagging discipline, cost visibility, and removing waste before it compounds. - Use AI agents as a normal part of daily workflow, and build them into the platform where they create leverage, including code review, alert triage, migrations, and repetitive remediation. - Automate toil out of the platform, reducing the number of requests that require a person at all, and measure whether it worked. - Treat documentation and developer onboarding as part of the deliverable, so teams can adopt platform capability without asking first. - Drive adoption of platform capability and standards, including the unglamorous migration work needed to move existing services. - Raise the engineering bar through code review and mentorship, and help other engineers get more out of AI tooling. QUALIFICATIONS - Five or more years of software or infrastructure engineering experience, including ownership of production systems that you also operated. - AWS to a depth that is genuinely second nature - reasoning about IAM policy evaluation, VPC routing and DNS, and container or serverless runtime behavior without reaching for documentation first, and having been the person who diagnosed it under real pressure. - Hands-on ownership of a multi-account AWS environment, including identity boundaries, networking, managed data services, and cost and tagging governance. - Infrastructure as code as your default rather than your fallback, in Terraform, CDK, or both, including how you review it, test it, and reason about blast radius before applying it. - Strong CI/CD engineering, including shared or templated pipelines consumed by more than one team. - Experience working in or closely with a platform, infrastructure, or developer experience team, and a clear point of view on what makes a platform get adopted rather than merely tolerated. - Real comfort with AI tooling in daily engineering work - using coding agents such as Claude Code to do meaningful multi-step work, able to describe where they fail and how you catch it, and keeping ownership of what ships. - Production operations experience: on-call, incident response, postmortems, and SLO thinking. - Strong programming ability in at least one language such as Python, Go, TypeScript, or a JVM language, enough to build tooling rather than only configure it. - Sound judgment on security, secrets, access control, and data handling, ideally in a regulated or audited environment. - Clear written communication - on a platform team, documentation and a well-argued change proposal are load-bearing, not overhead. PREFERRED QUALIFICATIONS - Healthcare or another regulated domain, including HIPAA and SOC 2 obligations. - Previous experience in a venture-backed high growth company. - GitLab CI at scale, including templated pipelines with typed inputs consumed across many repositories. - Datadog in depth: monitors, dashboards, SLOs, and keeping observability cost under control. - Container orchestration at production scale, such as ECS or Kubernetes. - Building AI tooling rather than only using it, such as MCP servers, Claude Code plugins or skills, or agent harnesses. - Dependency and currency automation, such as Renovate, and the lifecycle policy around it. - Delivery metrics work, including DORA, and the definitional arguments that come with it. - Familiarity with Team Topologies vocabulary and where platform teams fit alongside stream-aligned and enabling teams. - Experience decommissioning a platform capability, not only launching one. A LITTLE MORE ABOUT US: - We know healthcare. Our company was founded and is still led by industry veterans who have started and grown several market-leading companies in the space. - We have raised money from top tier investors who share the same long-term vision as we do of building an industry defining company that will endure over the long run. We are well capitalized. - Our clients love us. Customer satisfaction rates among employees using Gravie health plans consistently rank above 80% – nearly 40 points above the industry average. - Our culture is unique. We tend to be non-hierarchical, merit-driven, opinionated but kind people who thrive working in a high-performance, fast-paced environment. People at Gravie care deeply about making a positive impact in the lives of the people we serve. BENEFITS Our unique benefits program is the gravy, i.e., the special sauce that sets our compensation package apart. In addition to standard health and wellness benefits, Gravie’s package includes alternative medicine coverage, flexible PTO, up to 16 weeks paid parental leave, paid holidays, a 401k program, transportation perks, education reimbursement, and 2 days of paid paw-ternity leave. JOB APPLICANTS If you apply for employment with Gravie, personal information collected via our applicant tracking vendor is subject to our standalone California Job Applicant Notice at Collection, accessible directly within the application workflow and separate from this Policy.

Gravie44 дн назад

Staff Platform Engineer

Location: Remote Workplace: Remote Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way. ROLE SUMMARY Gravie is seeking a hands-on Staff Platform Engineer to do three things at once: build the golden roads and paved paths our engineering teams build on, define the north star architecture the organization moves toward, and design and deliver our AI-enabled capabilities. This is the hub role for technical direction at Gravie: long-term architectural direction and the standards that carry it converge here and radiate outward across teams. The ideal candidate combines deep distributed systems and AWS experience with real production generative AI experience, and is equally credible building a paved path teams voluntarily adopt and setting a target-state architecture the whole organization will inherit. They have established standards, reference implementations, and paved roads that engineers actually use, not just architecture documents. They will partner across Engineering, Product, Data, Security, and Infrastructure, and will work most closely with our Platform Engineering team, which is the primary vehicle for making change stick across the organization. At Gravie, engineers work closely with Product and own outcomes end to end, and we practice agentic development to increase the scope and leverage of that ownership: engineers use AI agents to help develop specs and plans, then to execute substantial multi-step engineering work, while setting guardrails and acceptance criteria and owning the quality of everything that ships. As agents take on more of the execution, the bar on problem framing, architectural judgment, system thinking, and risk identification gets higher, not lower. This is a deliberately split role: roughly 40% golden roads and paved paths (hands-on, reference implementations, shared libraries, service templates, scaffolding, pipelines - a path isn't paved until a team other than yours has shipped on it), 30% north star architecture (published decisions that get adopted and enforced by tooling, not just written down), and 30% AI solutions (agent systems, retrieval and context design, evaluation, and guardrails). The mix shifts by quarter. Being the hub does not mean being the bottleneck - a good year looks like teams making more decisions on their own because direction and the paved path are clear enough to follow without asking. The role is planned and reviewed on this split, not funded out of leftover capacity; part of the job is saying out loud when the split is breaking toward a support desk. KEY RESPONSIBILITIES - Build the paved paths teams build on, including reference implementations, shared libraries, service templates, scaffolding, and pipeline templates, so the recommended way is also the easiest way. - Treat the paved road as a product with real users: measure adoption and time to first success, and fix the parts people route around. - Do the first real implementation yourself, on a real workload with a real team, not in a demo repository. - Build the distributed systems patterns the paths encode: backend services, event-driven workers, job pipelines, APIs, and relational data models. - Partner with the Platform Engineering team as the primary delivery vehicle - build paved paths with them, plan into their roadmap, and hand off sustaining ownership of what becomes shared infrastructure. - Own migration and adoption, including moving existing services onto the path and retiring what it replaces. - Stay in code review across teams and close to production incidents as primary evidence of where the path is failing. - Define and publish the target-state architecture, with clear owners and decision dates, and keep it current as the business changes. - Own the AWS foundations product and AI workloads depend on: multi-account structure and identity boundaries, network and data isolation, compute and serverless runtimes, managed data services, and tagging and cost governance. - Drive standards adoption to completion - from documents into pipelines, with adoption measured across repositories and teams. - Lead buy, build, and reuse decisions, evaluating models, frameworks, and vendors on accuracy, latency, reliability, privacy, security, and cost evidence. - Reduce surface area as deliberately as you add it - consolidate overlapping tools and standards and own the sunset path for what gets replaced. - Design and build production-grade AI agent systems, including multi-agent workflows, orchestration layers, and supporting services. - Design retrieval, context, and memory systems that ground AI outputs, including how sensitive data is scoped, redacted, and audited. - Develop AI-powered decision-support systems meeting the accuracy, traceability, and explainability requirements of healthcare and other regulated environments. - Build product-quality user experiences using React and TypeScript where AI capability has to become understandable and actionable. - Establish patterns for AI quality assurance - automated evaluations, regression testing, groundedness checks, and compliance guardrails - so those requirements are met by default. - Establish one standard way to build, run, evaluate, and operate AI systems. - Act as the connective point across teams: surface duplicated effort, pick up decisions with no owner, and align teams heading toward the same problem. - Drive cross-team communication - writing, demos, and working sessions - so direction and tradeoffs are understood by teams not in the room. - Build alignment where incentives differ across teams, and surface disagreement rather than resolving it quietly. - Contribute to roadmap and capacity planning so paved path, foundational, and AI work is sequenced alongside product commitments. - Work through the Platform Engineering team to effect change at organization scale rather than standing up competing surfaces. - Facilitate collaboration between engineering teams, Product, Data, Security, and Infrastructure, including running forums where cross-cutting work gets planned and unblocked. - Represent technical direction to leadership and carry business context back to engineering. - Partner with Product to shape the roadmap and translate ambiguous business problems into architecture. - Raise the engineering bar through design review, mentorship, and a working decision-record practice, and make architectural context available to both engineers and agents. - Partner with Security, Compliance, and Data on AI governance, access control, and data handling, designed in rather than retrofitted. QUALIFICATIONS - Eight or more years of software engineering experience, including time with architecture scope and an organization-wide remit, and ownership of complex systems from design through production. - Still shipping: writes and reviews code today, can open a meaningful merge request in an unfamiliar codebase within the first weeks. - Experience building internal platform capability that other teams voluntarily adopted - can name the paved path or golden road built, how many teams moved onto it, and what they routed around. - Hands-on experience building and operating production AI applications, not only prototypes, including evaluation, monitoring, failure handling, guardrails, latency, and cost control. - Deep AWS expertise across a multi-account organization: identity and permission boundaries, networking, compute and serverless runtimes, managed data services, managed model services, and cost and tagging governance. Infrastructure as code in Terraform, CDK, or both. - A track record of establishing architectural foundations where little existed - specific standards introduced, how they were adopted by teams that did not report to the candidate, and which failed and why. - Hands-on experience with agentic software development - using AI coding agents to create specs and plans, execute meaningful multi-step work, run and fix tests, validate results, and iterate while owning architecture, quality, and what ships. - Deep expertise in at least one primary area (Python or JVM backend and distributed systems, or React and TypeScript product engineering), plus ability to work across the full system. - Experience with retrieval, embeddings, context, memory, evaluation, monitoring, or AI agent architectures and workflows. - Strong judgment in testing, monitoring, failure handling, privacy, security, access control, and data governance, carried into a regulated or audited environment. - Experience working with or inside a platform or infrastructure team as the delivery vehicle for organization-wide change, including how ownership was divided and how a parallel platform was avoided. - Experience serving as a central technical point across multiple teams, with evidence of making those teams faster rather than becoming their approval gate. - Demonstrated ability to create alignment across disagreeing teams, and to contribute to roadmap and capacity planning so foundational work is actually scheduled. - Clear written communication and effective collaboration across technical and non-technical teams; influence without authority is the primary tool of this role. PREFERRED QUALIFICATIONS - Healthcare, health insurance, or benefits administration, including ICHRA, claims, eligibility and enrollment, or interoperability standards such as EDI and FHIR. - Previous experience in a venture-backed high growth company. - Internal developer platform or developer experience work, including golden path or paved road programs and adoption measurement. - Experience with GitLab CI at scale, including shared and templated pipelines. - Production observability and LLM observability practice, including Datadog. - Event-driven and streaming architecture, such as Kafka or Flink. - Practical experience with agent tooling and standards such as Claude Code, the Model Context Protocol, and agent SDKs. - Experience defining an AI governance or technology radar process alongside a security or compliance partner. A LITTLE MORE ABOUT US: - We know healthcare. Our company was founded and is still led by industry veterans who have started and grown several market-leading companies in the space. - We have raised money from top tier investors who share the same long-term vision as we do of building an industry defining company that will endure over the long run. We are well capitalized. - Our clients love us. Customer satisfaction rates among employees using Gravie health plans consistently rank above 80% – nearly 40 points above the industry average. - Our culture is unique. We tend to be non-hierarchical, merit-driven, opinionated but kind people who thrive working in a high-performance, fast-paced environment. People at Gravie care deeply about making a positive impact in the lives of the people we serve. BENEFITS Our unique benefits program is the gravy, i.e., the special sauce that sets our compensation package apart. In addition to standard health and wellness benefits, Gravie’s package includes alternative medicine coverage, flexible PTO, up to 16 weeks paid parental leave, paid holidays, a 401k program, transportation perks, education reimbursement, and 2 days of paid paw-ternity leave. JOB APPLICANTS If you apply for employment with Gravie, personal information collected via our applicant tracking vendor is subject to our standalone California Job Applicant Notice at Collection, accessible directly within the application workflow and separate from this Policy.

Evotym44 дн назад

Location: Lithuania Workplace: fully About the Client Our client is a provider of online payment processing solutions, focusing on a range of services including payment gateway services, global payment processing, and open banking. They serve online businesses across various sectors, supporting multiple currencies and payment methods. Role Overview As a Data Engineer , you will have the opportunity to work with cutting-edge technologies and make a significant impact on data processes within the fintech sector. This role involves developing scalable data pipelines, optimizing data infrastructure, and collaborating with cross-functional teams to ensure data accuracy and quality. Key Responsibilities Work with various data sources and integrate them into internal systems. Develop, optimize, and maintain ETL pipelines and data ingestion processes. Take ownership of data lakes, including their maintenance and documentation. Transform complex data structures into machine-readable formats. Design and maintain databases and data infrastructure. Work closely with other departments to verify the relevance, quality, and accuracy of data. Identify opportunities to improve the reliability and efficiency of existing data processes. Requirements Strong proficiency in Python and advanced SQL. Hands-on experience designing, building, and maintaining scalable data pipelines and data processing solutions. Strong experience with ETL/ELT processes, including data ingestion, transformation, integration, and orchestration. Practical experience with Apache Spark for large-scale data processing. Experience with Apache Kafka and event-driven/streaming data pipelines. Hands-on experience with database design, data modeling, optimization, and performance tuning. Practical experience with AWS cloud services and cloud-based data infrastructure. Experience with Apache Airflow for workflow orchestration and scheduling. Practical experience with Docker and containerized environments. Strong knowledge of Git and modern software development workflows. Solid understanding of data architecture, data quality, reliability, scalability, and data engineering best practices. Ability to write clean, efficient, testable, and maintainable code. Strong analytical, problem-solving, and critical-thinking skills with excellent attention to detail. Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment. Languages: English, Russian Experience in fintech Key Soft Skills Strong analytical and problem-solving skills with excellent attention to detail. Ownership mindset, proactivity, and ability to work independently and manage priorities. Strong communication and collaboration skills with the ability to work effectively across teams. What We Offer Competitive salary aligned with experience Collaborative and transparent team culture High-impact role in a scaling fintech company Recognition of personal milestones and achievements Social benefits

Evotym44 дн назад

Location: Serbia Workplace: fully About the Client Our client is a provider of online payment processing solutions, focusing on a range of services including payment gateway services, global payment processing, and open banking. They serve online businesses across various sectors, supporting multiple currencies and payment methods. Role Overview As a Data Engineer , you will have the opportunity to work with cutting-edge technologies and make a significant impact on data processes within the fintech sector. This role involves developing scalable data pipelines, optimizing data infrastructure, and collaborating with cross-functional teams to ensure data accuracy and quality. Key Responsibilities Work with various data sources and integrate them into internal systems. Develop, optimize, and maintain ETL pipelines and data ingestion processes. Take ownership of data lakes, including their maintenance and documentation. Transform complex data structures into machine-readable formats. Design and maintain databases and data infrastructure. Work closely with other departments to verify the relevance, quality, and accuracy of data. Identify opportunities to improve the reliability and efficiency of existing data processes. Requirements Strong proficiency in Python and advanced SQL. Hands-on experience designing, building, and maintaining scalable data pipelines and data processing solutions. Strong experience with ETL/ELT processes, including data ingestion, transformation, integration, and orchestration. Practical experience with Apache Spark for large-scale data processing. Experience with Apache Kafka and event-driven/streaming data pipelines. Hands-on experience with database design, data modeling, optimization, and performance tuning. Practical experience with AWS cloud services and cloud-based data infrastructure. Experience with Apache Airflow for workflow orchestration and scheduling. Practical experience with Docker and containerized environments. Strong knowledge of Git and modern software development workflows. Solid understanding of data architecture, data quality, reliability, scalability, and data engineering best practices. Ability to write clean, efficient, testable, and maintainable code. Strong analytical, problem-solving, and critical-thinking skills with excellent attention to detail. Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment. Languages: English, Russian Experience in fintech Key Soft Skills Strong analytical and problem-solving skills with excellent attention to detail. Ownership mindset, proactivity, and ability to work independently and manage priorities. Strong communication and collaboration skills with the ability to work effectively across teams. What We Offer Competitive salary aligned with experience Collaborative and transparent team culture High-impact role in a scaling fintech company Recognition of personal milestones and achievements Social benefits

Evotym44 дн назад

Location: Portugal Workplace: fully About the Client Our client is a provider of online payment processing solutions, focusing on a range of services including payment gateway services, global payment processing, and open banking. They serve online businesses across various sectors, supporting multiple currencies and payment methods. Role Overview As a Data Engineer , you will have the opportunity to work with cutting-edge technologies and make a significant impact on data processes within the fintech sector. This role involves developing scalable data pipelines, optimizing data infrastructure, and collaborating with cross-functional teams to ensure data accuracy and quality. Key Responsibilities Work with various data sources and integrate them into internal systems. Develop, optimize, and maintain ETL pipelines and data ingestion processes. Take ownership of data lakes, including their maintenance and documentation. Transform complex data structures into machine-readable formats. Design and maintain databases and data infrastructure. Work closely with other departments to verify the relevance, quality, and accuracy of data. Identify opportunities to improve the reliability and efficiency of existing data processes. Requirements Strong proficiency in Python and advanced SQL. Hands-on experience designing, building, and maintaining scalable data pipelines and data processing solutions. Strong experience with ETL/ELT processes, including data ingestion, transformation, integration, and orchestration. Practical experience with Apache Spark for large-scale data processing. Experience with Apache Kafka and event-driven/streaming data pipelines. Hands-on experience with database design, data modeling, optimization, and performance tuning. Practical experience with AWS cloud services and cloud-based data infrastructure. Experience with Apache Airflow for workflow orchestration and scheduling. Practical experience with Docker and containerized environments. Strong knowledge of Git and modern software development workflows. Solid understanding of data architecture, data quality, reliability, scalability, and data engineering best practices. Ability to write clean, efficient, testable, and maintainable code. Strong analytical, problem-solving, and critical-thinking skills with excellent attention to detail. Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment. Languages: English, Russian Experience in fintech Key Soft Skills Strong analytical and problem-solving skills with excellent attention to detail. Ownership mindset, proactivity, and ability to work independently and manage priorities. Strong communication and collaboration skills with the ability to work effectively across teams. What We Offer Competitive salary aligned with experience Collaborative and transparent team culture High-impact role in a scaling fintech company Recognition of personal milestones and achievements Social benefits

Evotym44 дн назад

Senior AI/ML Engineer (LLM, Python)

Location: Poland Workplace: fully About the Client Our client is a provider of online payment processing solutions, focusing on a range of services including payment gateway services, global payment processing, and open banking. They serve online businesses across various sectors, supporting multiple currencies and payment methods. Role Overview As a Data Engineer , you will have the opportunity to work with cutting-edge technologies and make a significant impact on data processes within the fintech sector. This role involves developing scalable data pipelines, optimizing data infrastructure, and collaborating with cross-functional teams to ensure data accuracy and quality. Key Responsibilities Work with various data sources and integrate them into internal systems. Develop, optimize, and maintain ETL pipelines and data ingestion processes. Take ownership of data lakes, including their maintenance and documentation. Transform complex data structures into machine-readable formats. Design and maintain databases and data infrastructure. Work closely with other departments to verify the relevance, quality, and accuracy of data. Identify opportunities to improve the reliability and efficiency of existing data processes. Requirements Strong proficiency in Python and advanced SQL. Hands-on experience designing, building, and maintaining scalable data pipelines and data processing solutions. Strong experience with ETL/ELT processes, including data ingestion, transformation, integration, and orchestration. Practical experience with Apache Spark for large-scale data processing. Experience with Apache Kafka and event-driven/streaming data pipelines. Hands-on experience with database design, data modeling, optimization, and performance tuning. Practical experience with AWS cloud services and cloud-based data infrastructure. Experience with Apache Airflow for workflow orchestration and scheduling. Practical experience with Docker and containerized environments. Strong knowledge of Git and modern software development workflows. Solid understanding of data architecture, data quality, reliability, scalability, and data engineering best practices. Ability to write clean, efficient, testable, and maintainable code. Strong analytical, problem-solving, and critical-thinking skills with excellent attention to detail. Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment. Languages: English, Russian. Experience in fintech. Key Soft Skills Strong analytical and problem-solving skills with excellent attention to detail. Ownership mindset, proactivity, and ability to work independently and manage priorities. Strong communication and collaboration skills with the ability to work effectively across teams. What We Offer Competitive salary aligned with experience Collaborative and transparent team culture High-impact role in a scaling fintech company Recognition of personal milestones and achievements Social benefits

Компания не указана44 дн назад

Senior Platform Engineer

Задачи: — Build CI/CD as a product: unified pipeline templates in GitLab CI — Adopt GitOps approach to deployment — Reduce time-to-production and ... Требования: — Terraform — Ansible — Helm — GitLab CI (or equivalent experience with another CI) — Understanding of...

seniorГеография найма не уточнена

Компания не указана44 дн назад

Tech Lead NLP Engineer

Задачи: — Возглавить техническое развитие NLP в AI-продукте с большими языковыми моделями в production. — Оптимизировать скорость, стоимость и стабильность inference больших LLM. — Работать с distributed inference на multi-GPU / multi-node инфраструктуре (32×H200 + AWS). — Заниматься training и post-training LLM, развитием agent architectures. — Оказывать техническое лидерство NLP/CV-команде с перспективой роста до Head of ML. Требования: — Production-опыт оптимизации LLM inference: SGLang / vLLM / TensorRT-LLM. — Опыт работы с distributed inference/training больших моделей: MoE, tensor/expert/pipeline parallelism, multi-node GPU. — Знания KV cache, attention kernels, batching, quantization, GPU profiling. — Опыт training/fine-tuning и post-training: RLHF, DPO или аналоги. — Уверенное владение PyTorch и Transformers. — Английский язык на уровне B2 и выше. Условия: — Около 70% времени hands-on engineering, без Project/People Management. — Работа удалённая. — Сильная команда и высокое вознаграждение.

УдалённоleadГеография найма не уточнена

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