Вакансии

Задачи: — разработка на Python с использованием asyncio, FastAPI — работа с брокерами и таск-менеджерами: Kafka, RabbitMQ, Celery — оптимизация запросов и проектирование схем в PostgreSQL — реализация межсервисных коммуникаций через gRPC, REST, Webhooks — написание фронтенд-компонентов на TypeScript и React — применение DevOps-практик: Docker, CI/CD, развертывание в Kubernetes — использование инструментов мониторинга Prometheus и Grafana — обеспечение покрытия кода тестами Требования: — 3+ года коммерческого опыта на Python с глубоким пониманием asyncio, FastAPI — опыт работы с Kafka, RabbitMQ, Celery — уверенные знания PostgreSQL (query tuning, schema design) — практический опыт межсервисных коммуникаций: gRPC, REST, Webhooks — опыт написания фронтенд-компонентов на TypeScript / React — понимание DevOps-практик: Docker, CI/CD, Kubernetes — опыт работы с инструментами мониторинга Prometheus / Grafana — культура покрытия кода тестами — будет плюсом опыт работы с телеком-инфраструктурой, IP-телефонией, CTI или системами контакт-центров Условия: — full-time — b2b-контракт — почасовая оплата — удалённая работа, строго по России

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Remote; Japan (Remote); Slovakia (Remote); Spain (Remote); Czechia (Remote); Switzerland (Remote); Estonia (Remote); Singapore (Remote); Mexico (Remote); USA (Remote); Israel (Remote); Kyrgyzstan (Remote); Bulgaria (Remote); Austria (Remote); India (Remote); Iceland (Remote); Hungary (Remote); Ireland (Remote); Canada (Remote); South Africa (Remote); Brazil (Remote); Finland (Remote); UK (Remote); Romania (Remote); Cyprus (Remote); Malta (Remote); Latvia (Remote); Argentina (Remote); Poland (Remote); Uruguay (Remote) Workplace: Remote WHO WE ARE Hi, we're DuckDuckGo, the online protection company and remote-first team of 400+ on a mission to raise the standard of trust online. Founded in 2008 and profitable since 2014, our annual revenue is in the hundreds of millions USD and millions use our browser on Mac https://spreadprivacy.com/introducing-duckduckgo-for-mac/, Windows https://spreadprivacy.com/windows-browser-open-beta/, iOS https://spreadprivacy.com/duckduckgo-privacy-browser-ios14/, and Android https://spreadprivacy.com/introducing-app-tracking-protection/, our search engine https://duckduckgo.com/, and the DuckDuckGo subscription https://duckduckgo.com/pro. We also offer private, useful, and optional AI, including Duck.ai https://duck.ai/, which lets you chat privately with ChatGPT, Claude, and other AIs, all in one place.   Our culture https://duckduckgo.com/how-we-work of trust, inclusivity, and empowered project management underpins everything we do, where each team member takes full ownership of their projects, from scoping and execution to postmortem. If you're seeking end-to-end ownership of your work, you've come to the right place! YOUR TEAM AND ROLE Working on the Privacy Functional Team, you'll lead complex projects across key company-wide objectives. You'll protect the privacy of our users using Go, Node.js, Python, and (sometimes) Perl to write pragmatic, performant, testable code, and work on related projects. Potential projects include: - Designing systems for improving our search indexes and AI features without compromising user privacy - Building new features on top of our Tracker Radar https://spreadprivacy.com/duckduckgo-tracker-radar/ crawler to detect emerging privacy threats - Implementing new privacy features and maintaining existing ones, such as our Web Tracking https://duckduckgo.com/duckduckgo-help-pages/privacy/web-tracking-protections and Email https://duckduckgo.com/duckduckgo-help-pages/email-protection/privacy Protections   As a Senior Privacy Engineer, you'll own major components of our privacy technology, taking responsibility for their maintenance and improvement over time. A key part of the role will also be participation in our privacy review and audit processes, ensuring that all product developments align with our privacy principles. In time, you'll formally mentor other engineers, supporting their career growth and the growth of the Privacy Team at DuckDuckGo. ABOUT YOU - Ability to lead and collaborate on high-impact and complex projects from proposal through post-mortem. - Skills to wrangle vague problems, propose innovative solutions, and execute them effectively, involving others when necessary. - Experience working on privacy solutions in an engineering capacity (e.g. tracker blocker, differential privacy, or similar technology). - Deep technical understanding of online privacy threats and the existing solution space to protect against them. - Proficiency in any of the following: Go, Node.js, Python, Perl. - Excellent communication skills. You can validate your decisions and communicate them clearly to a variety of audiences, including non-technical stakeholders. COMPENSATION $178,500 USD annually and stock options. Compensation is transparent across the organization, and all team members within the same professional level and global region receive the same compensation.   Eligibility for company-sponsored health benefits is limited to team members based in the United States. This program does not extend to team members located in other countries, such as Canada or the UK.   Our Team Member Support Guide https://duckduckgo.com/assets/hiring/team_support_guide.pdf explains how we prioritize your wellbeing including paid parental leave, office setup, and co-working allowances. HIRING PROCESS Hiring works best when it's a two-way street. Learn how we help you get to know DuckDuckGo, envision your future role here, and find out more about how we hire https://duckduckgo.com/how-we-hire. DIVERSITY, EQUITY AND INCLUSION DuckDuckGo provides equal work opportunities to all team members and applicants, and it prohibits discrimination and harassment of any type on the basis of race, color, ethnicity, caste, religion, age, sex (including pregnancy), national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by our policies or federal, state, or local laws. We want to ensure that our hiring process is accessible. If you need reasonable accommodation for any part of the application process because of a medical condition or disability, please send an email to careers@duckduckgo.com to let us know the nature of your request. PLEASE NOTE THAT: - You’ll be required to attend meetings on camera via video conferencing - Expect to travel at least two times a year: once for our all-hands meetup and again for a team retreat (each around 4-5 days). While extenuating circumstances may impact attendance, everyone is strongly encouraged to attend. - While we offer a flexible work arrangement with no core hours, expect an average full-time commitment of 40 hours per week. - A successful candidate must pass a background check as a condition of joining the team. - By applying for this role, you confirm that all information submitted is accurate and complete. You further acknowledge that providing false or fraudulent information during the application process is cause for denial of an offer, revocation of any existing offer, or other adverse action, up to and including termination after the start of your commencement of work. Disclosure Statement: Use of AI in Hiring Process As part of our commitment to enhancing our recruitment process, we utilize artificial intelligence (AI) technology to assist in reviewing and summarizing job applications and test projects, including those tools integrated into our recruitment vendor platforms. We use AI to flag potentially fraudulent applications, analyze and summarize applicants’ experience, interviews, and project performance, and help streamline our selection process. Key Principles: - Data Privacy: All information provided in your application will be handled in accordance with our Recruiting Privacy Policy https://duckduckgo.com/static-assets/files/pages/careers/DuckDuckGo-Recruiting-Privacy-Policy-effective-September-30-2025.pdf. We ensure that your personal information is protected and used solely for recruitment purposes. - Human Oversight and Accountability: The AI technology is designed to support our hiring team by providing insights and summaries of applications and evaluations of test projects against scoring rubrics. All final evaluations and hiring decisions, however, will be made by our hiring team, who will consider the AI's input alongside other factors. - Transparency: We believe in transparency regarding our hiring practices. If you have any questions about how AI is used in our recruitment process, please feel free to reach out to us. By submitting your application, you acknowledge and consent to the use of AI technology in our review process. If you would like to request an alternative selection process, please contact us as at careers@duckduckgo.com. Thank you for your interest in joining DuckDuckGo! #LI-DNI

Location: Remote, Canada; Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role Data Products integrates GitLab and third-party SDLC data, and builds the dashboards, APIs, and data products that turn it into trusted, interoperable intelligence for customers and internal teams. This is a 0 to 1 team and we are hiring for a foundational engineering role. That means you bring technical leadership through ambiguity, helping figure out what to build, making the foundational design decisions, and keeping delivery moving while the picture is still forming. You own the full lifecycle of what ships and stand behind it. What you'll bring Proven ability to influence as a Staff owning product or platform systems end to end across multiple engineering and product teams. High agency, i.e finds the owner, clarifies the path, makes the call, and keeps pushing until the work lands. Force multiplier and raise the people and the codebase around them Deep full-stack expertise, including strong proficiency in Go, Ruby and Node . Depth in at least one of analytics and metrics platforms, graph data systems, CDC and warehouse integration. Hands-on experience building production AI-powered tool orchestration. Experience operating multi-tenant systems across SaaS and self-managed deployment. Direct communicator who builds alignment across teams. Bias to write things down and make reasoning legible. What you'll own Architect how GitLab and third-party data is ingested, modeled, and synced into the knowledge graph as a near-real-time graph of the development ecosystem. Lead integration with external systems for business context (Jira, observability tools, Zendesk, ServiceNow), indexed into the graph. Architect the Data Marketplace so customers consume GitLab data through Snowflake, Databricks, and BigQuery with no engineering dependencies. Publish GitLab observability as OpenTelemetry and broker the data-sharing APIs. Define operational readiness for everything the team ships across GitLab.com, Dedicated, and Self-Managed. Translate an ambiguous 0 to 1 problem space into concrete, iterable roadmaps in partnership with Product, Design, and the graph backend team. Resolve cross-team dependencies across graph backend, AI Platform, and Infrastructure without escalating everything upward. Mentor senior and intermediate engineers through design reviews, pairing, and code review. Create organizational leverage through speed. You identify systemic bottlenecks, simplify processes, and design systems that dramatically improve execution velocity. About the team Data Products work across three surfaces: Dashboard and Reporting Framework Builds AI-native dashboard components so customers and internal teams can see engineering health and GitLab's impact across DORA, VSA, AI Impact, SDLC trends, security, and quality, and launch their own custom dashboards on the same framework. Internal Ingestion Ingests third-party data into the GitLab and power tools like Knowledge Graph, building connectors so that signals from systems like Jira, Zendesk, ServiceNow, and observability tools become one queryable, near-real-time graph of the developer ecosystem. External Ingestion External makes GitLab and Knowledge Graph data consumable outside the product, through marketplace listings, data-sharing APIs and SDKs, and co-branded partner products. We are a new team in a highly ambiguous, high-impact space, and we need engineers who can create clarity while building, make practical tradeoffs, unblock cross-team dependencies, and keep pushing until meaningful customer value lands. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $152,800 — $259,200 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

☁️ Strong AWS experience: 3+ years designing cloud-native architectures on it. 🔧 6+ years architecting and running CI/CD (Jenkins, GitLab, GitHub Actions, Argo CD).

- Задачи: разработка платёжных микросервисов (ledger, KYC, anti-fraud, лимиты), проектирование схем данных и асинхронного обмена, API-контракты, unit/integration/e2e-тесты, релиз и разбор инцидентов. - Требования: глубокое понимание event loop, конкурентности, производительности;

• разрабатывать и оптимизировать 3D-визуализацию на WebGPU; • работать с LuciadRIA и большими наборами 3D-данных;

Looking for an engineer who has hands-on experience building AI agents that actually execute workflows, not just chat interfaces. You’ll work on agents that connect to APIs and internal systems, use tools, retrieve context, make decisions across multiple steps, and recover when something fails.

We are looking for an experienced Data Engineer to join a project focused on data engineering, pipeline development, and migration within a cloud-based environment. - Solid professional background in Data Engineering.

Важен опыт работы с платёжными системами.

Проект: Специалист привлекается для поддержки AI-команды (RnD AI Team), которая реализует портфель ИИ-инициатив для бизнес-подразделений компании на внутренней платформе Dify (LLM-агенты, RAG, классификация, извлечение данных из документов, интеграции через API/MCP). Подготовка тест-кейсов на основании требований и критериев приемки инициатив;

💎 Опыт управления рекламными бюджетами от $500к в месяц, AI-native, Strong English.

Looking for an engineer who has hands-on experience building AI agents that actually execute workflows, not just chat interfaces. You’ll work on agents that connect to APIs and internal systems, use tools, retrieve context, make decisions across multiple steps, and recover when something fails.

• Investigate and resolve technical and product-related issues. • Analyse logs, application behaviour and connectivity.

-Разбирать и обрабатывать документы в форматах Excel, PDF и сканы. -Извлекать табличные данные из документов.

Location: Lisbon Workplace: Hybrid ROLE HIGHLIGHTS: - Role: Forward Deployed Backend/DevOps Engineer - Location: Portugal (Lisbon) - Reports to: VP, Forward Deployed Engineering - Working type: Hybrid (On-site at customer locations approx. 50% of time) - Must be eligible and able to attain relevant national and NATO security clearances. - Must be based in-country and native level speaker of the country you're deploying to.   WHY THIS ROLE MATTERS In this role, you are the backend and infrastructure backbone defence and intelligence customers rely on. You deploy ICEYE's intelligence platform into secure (often air-gapped) environments and design the high-performance backend systems that move satellite data at scale — fast APIs, resilient services, tuned databases, automated infrastructure. Our stack: Python and Go services on Kubernetes, PostgreSQL and MongoDB, Terraform and Ansible, CI/CD pipelines that also run offline, cloud (AWS) and on-premises air-gapped clusters, with AI-assisted development as the default. This is a ship-daily role: systems you build run mission-critical workloads analysts use to protect nations. WHAT YOU'LL OWN Platform deployment & operations - Deploy ICEYE's intelligence platform into customer secure environments (on-premise, air-gapped networks, classified data centres). - Install, upgrade and operate Kubernetes clusters and containerised applications (Docker) in customer environments — cloud and on-premises. - Build infrastructure-as-code (Terraform, Ansible) and CI/CD pipelines that work without external internet access, including offline registries and artifact mirroring. - Troubleshoot deployment and production issues in disconnected environments, from the application layer down to storage and networking. High-performance backend systems - Design and build APIs, services and data pipelines that handle large volumes of SAR imagery and intelligence data with low latency. - Connect ICEYE systems to customer C4ISR infrastructure and intelligence tools through robust, well-designed integrations. - Own database design and performance at production scale (PostgreSQL, MongoDB): schemas, indexing, tuning, replication and backup. - Apply caching, queueing and horizontal scaling patterns deliberately — and capacity-plan for fixed on-premises hardware where you cannot simply scale out. Reliability, security & observability - Build monitoring, logging and alerting that work in restricted environments, so problems are found before the customer finds them. - Harden deployments to meet customer security constraints; own backup, recovery and upgrade paths for systems that cannot go down. - Run the infrastructure behind AI workloads shipped by the team — model serving, vector databases, GPU nodes — reliably and repeatably. Customer engagement & rapid delivery - Work embedded with intelligence analysts, operators and customer IT teams to understand operational and infrastructure requirements. - Rapidly prototype and iterate (working systems first, polish later) to address immediate operational needs. - Conduct hands-on training for customer administrators on platform operations. - Feed deployment experience back to ICEYE's product and engineering teams as concrete technical requirements. WHAT WE'RE LOOKING FOR Must-haves - 5+ years hands-on backend engineering building and operating production systems at scale. - Strong programming skills in Python. You write typed, tested services and you are comfortable with async APIs. TypeScript is a plus. - Proven experience designing high-performance backend systems: APIs, distributed services, messaging, caching and databases under real load. Experience with spatial, search, or vector workloads in Postgres is a plus. - Production experience in both cloud (e.g., AWS) and on-premises environments — this role lives in both. - Deep Docker and Kubernetes: deploy, debug, and upgrade clusters — not only consume them. GitOps and infrastructure-as-code experience. Pipelines that can run offline or on an isolated network are a plus. - Observability as a habit: metrics, logs, and alerts are part of done. - Infrastructure-as-code (Terraform, Ansible) and CI/CD experience, ideally including pipelines that run offline or in isolated networks. - PostgreSQL and/or MongoDB at production scale: schema design, tuning, replication, backup and recovery. - Observability as a habit: metrics, logging and alerting are part of done, not an afterthought. - Builder mindset: ship fast, iterate with feedback, and prioritise working solutions over perfect architecture. - Strong communication and customer engagement, able to work embedded with intelligence analysts / military operators. - Strong spoken and written English, able to explain technical concepts to non-technical stakeholders. - Willingness to travel 40–60% to customer sites within the region. Nice to have - Experience in defence, intelligence, or government operational environments. - Deploying to on-prem data centres and/or air-gapped networks, including offline artifact and registry management. - Event streaming and high-throughput data systems (e.g., Kafka or similar) at production scale. - Deeper Kubernetes: operators, service mesh, multi-cluster, storage and networking internals. - Security hardening and accreditation experience (system hardening baselines, audit and compliance in classified environments). - Familiarity with geospatial data, satellite imagery, or remote sensing. - Experience running infrastructure for AI/LLM workloads: GPU scheduling, model serving, vector databases. - Active use of AI-assisted development tools as part of your daily workflow. - Experience in defence tech (e.g., Palantir, Anduril, Scale AI, similar startups). WORKING AT ICEYE At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win. HOW WE WORK (OUR VALUES) Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way. - Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions. - See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others. - Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions. - Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift. - Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins. BENEFITS Our benefits are designed to support your health and wellbeing—at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process. OUR COMMITMENT TO DIVERSITY, EQUITY AND INCLUSION We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table. We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help. Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at https://www.iceye.comiceye.com http://iceye.com