Вакансии

Вы будете создавать и внедрять решения на базе генеративного ИИ: мультиагентные системы, RAG-конвейеры, интеграции с внутренними сервисами и автоматизацию бизнес-процессов. Строить и сопровождать RAG-решения: векторные хранилища, пайплайны, ретривал.

Сопровождение и администрирование кластеров kubernetes (прикладные java, nodejs); Что важно:

В АО «Bereke Bank» открыты вакансии для IT-специалистов в различных продуктах с разнообразными задачами. Предлагается гибридный формат работы (удаленка и офис). В AI команду требуются: — Senior Data Scientist — Middle Fullstack (честное разделение 50/50 между фронтендом и бэкендом) — Senior Fullstack (с уклоном в бэкенд на Python) — ML-инженер Для отклика и получения деталей по проектам необходимо связаться через Telegram @dashan13 с резюме.

— Тестировать новые функции и доработки. — Проводить функциональное и регрессионное тестирование.

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: United States; Canada; United Kingdom; Germany; The Netherlands Workplace: Remote ClickHouse Labs is an applied research group that works closely with engineering teams to identify challenging problems in database systems and develop novel solutions. Our goal is to advance the state of the art and translate research ideas into real-world system improvements. ABOUT THE ROLE As a Research Scientist at ClickHouse Labs, you will work on high-impact problems at the intersection of database systems, distributed systems, AI, and systems software engineering while collaborating with experienced researchers and engineers. The role covers a broad range of problems across database systems research and engineering, including but not limited to query optimization, execution engines, distributed systems, consensus protocols, materialized views, indexing and data structures, benchmarking, workload analysis, and emerging agentic workloads. In this role, you will work on leading open-source database systems (e.g., ClickHouse, PostgreSQL). You will investigate performance and scalability bottlenecks in production-quality systems, develop new algorithms and system designs, and evaluate them under realistic workloads and hardware configurations. Projects may involve improving query planning and execution, reducing CPU and memory overhead, increasing throughput and concurrency, scaling across distributed deployments, or redesigning system components to leverage modern hardware better. You will work closely with engineers to turn successful ideas into robust implementations that can be deployed and maintained in widely used open-source systems. Researchers are encouraged to publish their work, contribute to open-source projects, participate in leading research conferences, and pursue ambitious projects with strong engineering and research support. RESPONSIBILITIES - Conduct applied research aimed at advancing the performance, scalability, reliability, and functionality of open-source database management systems, including ClickHouse and PostgreSQL. - Design and evaluate new algorithms, data structures, and system architectures for areas such as query optimization, indexing, storage management, query execution, and distributed data management. - Develop high-quality research prototypes and production-grade implementations, and work with engineering teams to integrate successful techniques into open-source and production systems. - Collaborate closely with database engine, infrastructure, and data-plane engineers to identify high-impact research problems and translate research results into practical system improvements. - Analyze large-scale database workloads, performance traces, benchmarks, and production telemetry to identify bottlenecks and optimization opportunities. - Design rigorous experiments and benchmarks to evaluate new techniques across realistic workloads, hardware configurations, and deployment environments. - Communicate research results through technical reports, open-source contributions, internal presentations, and publications at leading database and systems venues (e.g., SIGMOD, VLDB, CIDR). - Contribute to the broader technical direction of ClickHouse Labs by identifying emerging research opportunities and helping shape longer-term research projects. QUALIFICATIONS MINIMUM QUALIFICATIONS - Masters/Ph.D. in Computer Science, Data Science, or a related field, with research experience in databases, distributed systems, systems software, or machine learning systems. - Strong publication record in leading peer-reviewed research venues such as SIGMOD, VLDB, SOSP, OSDI, NSDI, or NeurIPS, with work relevant to databases or systems. - Strong programming and systems-development background, including experience working with database internals or other large systems codebases such as PostgreSQL, MySQL, ClickHouse, or comparable modern DBMSs. - Proficiency in systems programming using C, C++, Rust, Zig, or a comparable low-level programming language. - Strong analytical, experimental, problem-solving, and technical communication skills. PREFERRED QUALIFICATIONS - Experience developing or operating database systems in cloud environments such as AWS, Google Cloud Platform, or Microsoft Azure. - Familiarity with large language models, reinforcement learning, learned optimization, or other techniques at the intersection of machine learning and systems. - Experience contributing to large open-source systems projects, particularly database engines, distributed systems, compilers, or storage systems. BONUS QUALIFICATIONS - Familiarity with modern systems-development and verification tooling, including formal specification or model-checking frameworks such as TLA+/PlusCal, Alloy, or Lean, as well as relevant development tools and languages such as LLVM, eBPF, Python, Go, or advanced profiling and debugging tools (e.g., perf, sanitizers, or tracing frameworks). - Proven track record in optimizing database systems via hardware acceleration (e.g., GPU, FPGA, CXL), with a focus on designing methods for offloading for query execution, storage layers, and data-intensive primitives using frameworks like CUDA, SYCL, ROCm, or specialized FPGA development flows. PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new team member who joins our company receives stock options. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: Berlin adjoe builds the technologies behind mobile app growth and monetization, challenging the status quo of adtech by redefining how apps acquire users and generate revenue. Our two products, Playtime Arcade and adjoe Ads, sit at opposite ends of the mobile advertising spectrum: rewarded engagement and programmatic in-app advertising. Together they run on 13% of the world’s smartphones, connecting advertisers, publishers, and hundreds of millions of users, with one goal: making mobile advertising work better for everyone in the ecosystem. Next-Gen Machine Intelligence for Auctions & Real-Time Bidding The Data Science Research team builds the foundational algorithms that govern real-time bidding, budget pacing, and allocation mechanics across our products. Operating as a horizontal deep-tech layer, the team leverages advanced AI—specifically reinforcement learning, objective-based optimization, and control theory—to deliver critical systems that perfectly harmonize advertiser ROI, platform health, and user relevance. These frameworks scale adjoe’s products beyond standard industry limitations, utilizing behavioral and transactional data from over 770 million users. The team operates at the intersection of high-load, low-latency engineering, auction mechanism design, and state-of-the-art machine learning. Acting as the company’s internal science bar raiser, they translate complex business constraints into mathematical objectives. The stack is optimized for high throughput: PyTorch for training deep learning and RL models, distributed architectures for large-scale Bayesian inference, Trino and Athena for querying massive user-behavior data lakes, and dedicated GPU infrastructure. The full lifecycle of these AI capabilities is owned by the team—from academic paper translation and offline prototyping to deploying core algorithmic frameworks built to sustain ultra-low latency, real-time production environments evaluating tens of thousands of requests per second. Your Mission & Who We Are Looking For: - PhD or equivalent research depth, with a production track record. You have a PhD in CS, Statistics, Math, or Physics (or an MSc with extensive industry research) and a track record of peer-reviewed publications in fields like Computational Advertising, Auction Theory, Mechanism Design, or Game Theory. You’ve spent 4+ years optimizing complex systems through automated algorithms in AdTech, marketplaces, or high-frequency trading. - You translate research into production, not just prototypes. You distill SOTA research into robust, production-ready code optimized for sub-millisecond latency. At adjoe, that means architecting sophisticated auction mechanics, real-time bidding engines, and allocation algorithms serving millions of requests across a stack built on 770 million users. - Deep expertise in Reinforcement Learning and Optimization. You have hands-on, deep theoretical and technical expertise in objective-based optimization, control theory, or RL applied specifically to bidding, pricing strategy, and budget pacing under extreme production constraints. - Mastering high-dimensional data at scale. Large-scale Bayesian inference and handling sparse, high-dimensional advertising data are core to your work. You are strongly proficient in Python and related stacks, and know exactly how to architect scalable data pipelines to feed real-time optimization models. - Raises the technical bar around them. You lead architectural debates, collaborate tightly with Product Managers and Engineers to map business goals to mathematical objective functions, mentor junior scientists, and drive the team’s research direction without needing a roadmap handed to you. 🌎 WE WELCOME APPLICATIONS FROM TALENT WORLDWIDE AND PROVIDE RELOCATION SUPPORT TO HAMBURG, GERMANY FOR THOSE READY TO JOIN OUR TEAM. What’s in It for You? At adjoe, you’re not here to just close JIRA tickets, you’re helping build the infrastructure behind one of the most impactful platforms in adtech. The systems you work on will reach hundreds of millions of users and power billions of decisions every day. - Go Big. Own projects with impact on 770M users and push adtech boundaries. - Move Fast. Ship solutions multiple times a day, learn from results, and keep momentum. - Be Direct. Solve problems openly and collaborate across teams. - Thrive Together. Grow with a diverse, global team of people from over 40 different countries that learn from each other. - Have Fun. Celebrate wins, enjoy daily victories, and bring your energy. We welcome applications from people who will contribute to the diversity of our company.

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.

Location: Office, EU We are the creators of a new fintech era! Our mission is to revolutionize the world by making blockchain technology accessible to everyone in everyday life. WhiteBIT is a global team of more than 1,500 professionals united by a shared vision of shaping the future. We are building our own blockchain ecosystem to ensure maximum transparency and security for over 8 million users worldwide. Our cutting-edge solutions, rapid adaptation to market challenges, and technological excellence set us apart from traditional companies. Our official partners include Juventus, FC Barcelona, Lifecell, FACEIT, and VISA. Join us as an IT Helpdesk Specialist! Requirements Experience: 2+ years of experience in IT Helpdesk, IT Support, or IT Systems Administration, with practical exposure to networking. L1 IT Support: Hands-on experience providing first-line technical support, troubleshooting hardware, software, connectivity, and user access issues. Networking Equipment: Experience configuring and administering Juniper, Fortinet, and Cisco equipment (routers, switches, firewalls, and wireless APs). Network Security & Protocols: Understanding of Fortinet NGFW, VPNs (IPsec/SSL), BGP/OSPF, VLAN segmentation, and basic network troubleshooting. AAA & 802.1X: Familiarity with RADIUS (FreeRADIUS, Cisco ISE, or FortiAuthenticator) and 802.1X network access control. IT Services & SaaS: Experience supporting and administering corporate SaaS environments, including Google Workspace, Atlassian Suite, Slack, and SSO/IdP solutions. Scripting & Automation: Basic scripting skills (Bash, Python, or PowerShell) and familiarity with APIs / Webhooks for workflow automation (Jira, Slack, CRM integrations). Nice-to-Have: Vendor certifications such as CCNA/CCNP, JNCIA, or Fortinet NSE 4/5. Responsibilities L1 IT Support: Provide first-line technical support to employees, troubleshooting hardware, software, network connectivity, and access-related issues. Ticket Management: Handle incoming IT requests, prioritize incidents, track resolutions, and escalate complex issues to L2/L3 support when necessary. User Access & SaaS Administration: Manage user accounts, access permissions, onboarding/offboarding requests, and basic troubleshooting across Google Workspace, Atlassian Suite, Slack, and SSO/IdP. Network Support: Assist in configuring, maintaining, and troubleshooting Juniper, Fortinet, and Cisco network equipment (routers, switches, wireless APs, and firewalls). Network Security & Access Control: Support VPN connectivity (IPsec/SSL), VLAN configurations, and network authentication systems (RADIUS, 802.1X). IT Operations & Monitoring: Monitor network and IT service health using tools such as Zabbix, Syslog, and SNMP, identifying and escalating potential issues. Workflow Automation: Support the development and maintenance of automation scripts (Bash, Python, or PowerShell) and API integrations between internal services. Incident Management & SLAs: Ensure timely resolution of L1 incidents, maintain service quality, and escalate complex network or infrastructure issues in line with established SLAs. Documentation: Maintain IT support documentation, troubleshooting guides, and internal knowledge-base articles. Working terms Immerse yourself in Crypto & Web3: Master cutting-edge technologies and become an expert in the most innovative industry. Work with the Fintech of the Future: Develop your skills in digital finance and shape the global market. Take Your Professionalism to the Next Level: Gain unique experience and be part of global transformations. Drive Innovations: Influence the industry and contribute to groundbreaking solutions. Join a Strong Team: Collaborate with top experts worldwide and grow alongside the best. Work-Life Balance & Well-being: Modern equipment. Comfortable working conditions, and an inspiring environment to help you thrive. 22 business days of paid leave. Additional days off for national holidays. Health insurance. Your privacy matters to us. You can learn more about how we handle your personal data with regard to your application in our Candidate Privacy Notice .

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.

• Развитие поставок данных. Проектировать и развивать RT/NRT‑поставки данных для разных сценариев работы продуктовых команд — например, для жизненного цикла заказа, работы сотрудников, складских операций и отслеживания доступности товаров.

Location: Нижний Новгород Приглашаем в команду аналитика по макроэкономическим исследованиям и региональной экономике. Вы будете определять макроэкономические предпосылки, потенциал и системные риски для планирования развития бизнеса. Обязанности ### Ключевые задачи: * Анализ данных и моделирование: регулярная агрегация и обработка данных по ключевым макроэкономическим показателям субъектов РФ. Построение и верификация эконометрических моделей (ARIMA, VAR, DSGE, QPM) с использованием Python (scikit-learn, statsmodels), R, EViews или Stata. * Декомпозиция и прогнозирование: адаптация макроэкономических прогнозов ЦМИ до уровня конкретных регионов, сценарное прогнозирование (базовый, стрессовый, оптимистичный) и оценка мультипликативных эффектов. * Оценка влияния: анализ влияния федеральных инфраструктурных проектов, национальных проектов и государственных программ на экономику регионов и бизнес Банка. Оценка бюджетной обеспеченности субъектов РФ и зависимости от федеральных трансфертов. * Верификация и полевые исследования: мониторинг ключевых событий в субъектах РФ, анализ посланий губернаторов. Организация совместных мероприятий с региональными стейкхолдерами (ТПП, министерства) для проверки гипотез. * Визуализация и автоматизация: работа с геоинформационными системами (ГИС) для визуализации пространственных данных, использование внутрибанковских AI-инструментов, парсинг данных Росстата и Минэкономразвития. Требования ### Наши ожидания: * Высшее профильное образование (экономика, финансы, физико-математические науки). * Опыт работы от 1 года в роли аналитика с подтвержденным портфолием (наличие примеров кода). * Умение работать с большими массивами данных и строить прогнозы. * Продвинутое владение Python,MATLAB, Quarto, SQL, Excel, понимание методологии очистки и нормализации данных. * Владение эконометрическими пакетами (EViews, Stata, R) и Python-библиотеками (statsmodels, scikit-learn). * Практический опыт построения эконометрических моделей (ARIMA, VAR, DSGE, QPM) * Владение математическими методами в экономике и статистическом анализе. * Глубокое знание макроэкономики, бюджетной политики РФ и структуры национальных проектов. * Уровень владения английским языком —Intermediate. Условия ### Мы предлагаем: * Стабильный оклад и социальную поддержку сотрудников. * Официальное оформление с первого дня. * Адрес офиса ул.Октябрьская,35. * График работы с 9-18. * Расширенный ДМС с первого дня для сотрудников и льготное страхование для близких. * Корпоративное обучение в Виртуальной школе Сбера, регулярные внутренние и внешние митапы. * Корпоративную пенсионную программу. * Современный офис с зонами отдыха и спортзалом. * Подписка Прайм с возможностью совместного использования на трёх близких.

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;

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.

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

-👥 Управление и развитие команды (Backend, Frontend, Mobile (iOS/Android), QA): -Проводить 1:1, давать честную обратную связь, составлять планы развития.