Вакансии

Design, write, and maintain high-quality Apex code, custom LWC user interfaces, and asynchronous backend processing frameworks. Architect and build secure, bidirectional integrations between Salesforce and third-party enterprise platforms (e.g., SAP, HubSpot, BigQuery) using REST/SOAP APIs and middleware.

Location: Bangkok, Thailand; Vietnam; Georgia; Indonesia; Philippines Social Discovery Group (SDG) is a group of social discovery companies. SDG solves the problems of loneliness, isolation, and disconnection - transforming virtual intimacy into the new normal. SDG’s products redefine the way people interact and connect with one another. Our portfolio includes social entertainment platforms designed to connect people online across different cultures and regions of the world. We bring together a team of like-minded people and IT professionals who specialize in creating and developing globally impactful social discovery products. Our international team of digital nomads works remotely from all over the world. We’re proud to be a two-time “Great Place to Work” winner (USA & Japan, 2024–2025) and a Top-5 Company for Work-From-Anywhere Jobs (FlexJobs, 2025). We are looking for a Lead PPC Specialist . Your main tasks will be: Own and evolve the global Paid Search strategy across our products portfolio, with primary responsibility for Google Ads and additional ownership of Microsoft Advertising where relevant; Lead, coach and develop the Paid Search team: set objectives, allocate workload, review performance, improve execution standards and support hiring and onboarding; Own portfolio-level budget allocation, forecasting and performance across products, markets and funnels, including Tier-1 geographies and six-figure+ monthly search spend; Design scalable account and campaign structures covering keyword strategy, match types, negative keywords, search-query mining, bidding, RSAs, DSA and Performance Max where appropriate; Lead the implementation and optimization of pLTV-based conversions and conversion values: partner with Analytics and Engineering to validate predicted lifetime value, conversion mapping, value uploads and signal quality, then use these signals in value-based Smart Bidding; Establish a rigorous experimentation program using Google Ads Experiments to A/B-test bidding strategies, campaign structures, match types, ads, landing pages and pLTV optimization approaches; Apply sound statistical methods to experiment design and interpretation, including hypothesis definition, sample-size and power considerations, confidence intervals, significance, seasonality and test contamination; Oversee conversion tracking, Enhanced Conversions, offline conversion imports, attribution and reporting across app, mobile web and desktop; identify data gaps and drive remediation; Analyze search demand, auction dynamics, marginal returns and conversion-funnel performance to identify scalable growth opportunities and present decision-ready recommendations; Collaborate with Analytics, Engineering, Product, CRO, Creative and Localization teams to improve acquisition economics and landing-page performance across international markets. We expect from you: Expert-level knowledge of Google Ads Search, including account architecture, keyword and search-query strategy, match types, negative keywords, RSAs, DSA, auction dynamics, policy compliance and international scaling; Advanced knowledge of Google Smart Bidding: Maximize Conversions, Target CPA, Maximize Conversion Value, Target ROAS, portfolio bid strategies and the conditions required for stable learning and scale; Hands-on experience implementing and optimizing campaigns against predicted lifetime value (pLTV) conversions or conversion values, including offline conversion imports, Enhanced Conversions, API-based value uploads, signal validation and bidding calibration; Proven experience designing and running A/B tests through Google Ads Experiments, including bidding, campaign structure, match-type, ad-copy and landing-page experiments; Strong statistical foundation: hypothesis formulation, experimental design, sample-size and power concepts, confidence intervals, statistical significance, variance, seasonality, multiple-testing risk and common sources of bias; Advanced understanding of performance economics and funnel metrics: CAC/CPA, pLTV, ROAS, LTV-to-CAC, payback period, conversion lag, marginal CPA/ROAS, CVR, CPC, CTR and impression share; Deep understanding of measurement and attribution across app, mobile web and desktop, including GA4, Google Tag Manager, Enhanced Conversions, offline conversion tracking and data-driven attribution; Advanced Excel/Google Sheets skills and working proficiency with SQL, BigQuery and BI tools for campaign, cohort and funnel analysis; Strong capability in budget allocation, forecasting, scenario modelling, marginal-return analysis, pacing and executive-level reporting for high-spend international accounts; Experience with Microsoft Advertising, Search Ads 360, Google Ads Scripts or other automation tools is a strong advantage; Proven ability to build scalable campaign-management, experimentation, analytics and quality-assurance processes for a team. What do we offer: REMOTE OPPORTUNITY to work full-time; The initial pay level or pay range for this role will be shared with candidates during the recruitment process and before the commencement of employment; Vacation 28 calendar days per year; 7 wellness days per year (time off) that can be used to deal with household issues, to lie down and recover without taking sick leave; Bonuses up to $5000 for recommending successful applicants for positions in the company; 50% payment for professional training, international conferences, and meetings; Corporate discount for English lessons; ​ Health benefits. According to the paychecks, if you are not eligible for corporate medical insurance, the company will compensate you with up to $ 1,000 gross per year per employee. This can be spent on self-purchase of health insurance or on doctor’s fees for yourself and close relatives (spouse, children); ​ Workplace organization. The company provides all employees with an equipped workplace and all the necessary equipment (table, armchair, wifi, etc.) in our offices or co-working locations. In the other locations, the company provides reimbursement of workplace costs up to $ 1000 gross once every 3 years, according to the paychecks. This money can be spent on the rent of the co-working room, on equipping the working place at home (desk, chair, Internet, etc.) during those 3 years; Internal gamified gratitude system: receive bonuses from colleagues and exchange them for our merchandise, team building activities, massage certificates, etc. Sounds good? Join us now!

Опыт аналитики мобильных игр от 4 лет, сильная экспертиза в продуктовых и F2P-метриках (Retention, LTV, ARPU/ARPDAU, ROAS). Уверенный SQL, Python, опыт A/B-тестирования и работы с BI.

Location: Tbilisi, Georgia Workplace: remote Novakid is an online English school for kids. The Visitor Squad owns the funnel from first visit to paid lesson: landing pages, registration and booking, blog, and the visitor's personal account. We track two key metrics, visitor → lead and visitor → booking, and test almost everything we ship through experiments. This is a backend-first role: about 70% backend (Python, some Node.js ) and 30% frontend (React/TypeScript). You'll own the server side of the funnel: APIs behind registration, booking and the visitor account, attribution, and server-side integrations with ad platforms. When a feature needs frontend work, you'll take it end-to-end yourself. You'll work within the squad's processes: planning, grooming, code review, sprints and releases. Key Responsibilities Design, build and maintain Python services and APIs behind the funnel: registration, booking and the visitor account Build and own server-side integrations with ad platforms (Meta CAPI, TikTok Events API and similar): event delivery, deduplication, retries and monitoring Own attribution on the backend: end-to-end visit tracking, UTM persistence, and diagnosing gaps in V2L/V2B data Keep services fast and reliable: caching, query optimization, debugging server-side rendering in our Node.js/Express layer Investigate production incidents through Sentry and see them through to resolution Support A/B testing on the server side: feature flags, rollouts, and cleanup after experiments Ship frontend changes in React/TypeScript when a feature needs it Requirements 5+ years of production backend development in Python, including services you designed, shipped and supported in production yourself Strong backend fundamentals: API design, relational databases and SQL, caching, background jobs, and integrating third-party APIs (auth, rate limits, retries, idempotency) Production debugging experience: logs, error tracking, finding the root cause of an issue Node.js : comfortable reading and changing an Express service Working frontend skills: enough TypeScript and React to deliver a feature end-to-end. Expert level not required. An understanding of how tracking data travels from click to report, and the ability to find where it gets lost Product mindset: you work with metrics and hypotheses, not just tickets Fluent English Nice to Have Server-side conversion tracking (CAPI, Events API, server-side GTM) BigQuery: checking your own hypotheses with data CDN and edge caching (CloudFront or similar) GDPR and consent mechanisms E2E testing with Playwright or Cypress Our Tech Stack Python, Node.js , Express (SSR), TypeScript, React, Next.js , Sentry, GrowthBook, BigQuery. What we offer Fully remote work A chance to shape the future of AI-driven education A fun, dynamic team that loves building innovative products Unlimited holidays, because we trust you to deliver

Location: Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Almaty, Kazakhstan Workplace: OnSite Almaty, Kazakhstan · Trust & Safety Engineering Why this role exists Higgsfield builds tools for AI creators: creative workflows, collaborative projects in Cinema Studio, and a community for sharing work. We need a backend engineer who combines deep database and architecture expertise with the initiative to own product outcomes, from the first idea to production. What you'll actually do - Build AI creation workflows, Cinema Studio collaboration, and community features: sharing, discovery, contests, profiles, and follows. - Own features and bugs through release and follow-up. Define API contracts and coordinate frontend work to deliver the complete feature. - Deliver responsive UX: live progress, timely updates, and clear failure recovery, even when it takes more engineering effort. - Remove database and service bottlenecks under high traffic; improve correctness, latency, and reliability. - Define success metrics, build dashboards and alerts, and track adoption and system health after launch. Must-haves 1. Deep database expertise: data modeling, indexes, query plans, partitioning, transactions, and locking. You diagnose production bottlenecks and understand the trade-offs behind your fixes. 2. Strong architecture judgment: service boundaries, APIs, asynchronous workflows, caching, and failure handling. Hands-on Kubernetes and CI/CD experience, from design and testing to deployment and debugging. 3. A track record of shipping and operating backend systems across multiple projects, including high-traffic workloads. You can explain your decisions and measurable results. 4. API security: authentication, object-level authorization, and tenant isolation. You prevent IDOR in reads and writes, including resource IDs in request bodies, and test access across users and workspaces. 5. You've built or studied social networks and collaborative tools, and can explain design trade-offs in feeds, follow graphs, notifications, sharing permissions, real-time sync, and concurrent edits. 6. High agency and ownership: you find worthwhile problems, propose improvements, remove blockers, and follow through without waiting for assignments. 7. Low ego: you welcome direct feedback, admit mistakes, change your mind with evidence, and help wherever the team needs you. 8. Ambition and optimism: hard goals and rapid growth energize you. You challenge assumptions constructively and commit to making ambitious ideas work. 9. Energy for an intense pace, shifting priorities, and urgent launches or production issues that can require work outside regular hours. Our tech stack - Languages and frameworks: Python (FastAPI), Go (Gin), and TypeScript (React/Next.js on the frontend). - Data: PostgreSQL and Redis; analytical databases such as BigQuery or ClickHouse. - Messaging: Kafka, Amazon SQS, and RabbitMQ; event-driven services and asynchronous workers. - AWS: S3, DynamoDB, ECR, and Secrets Manager. - Infrastructure: Docker, Kubernetes on Amazon EKS, Helm, Argo CD, and GitHub Actions. - Observability: Prometheus, Grafana, OpenTelemetry, and Sentry. Interview process 1. Recruiter screen: role expectations, logistics, and fit. 2. Technical interview: a difficult system you shipped, what you personally decided and built, and how your work made the team more capable without leaving it dependent on you. 3. Additional technical interview, if needed: a deeper discussion of technical skills and open questions. 4. Product fit interview: turn a user problem into a small first release, decide what to leave out, and explain how feedback would change the solution. 5. Offer. What we offer Competitive base in USD, equity, relocation support, and ownership of product experiences that directly shape how people create with AI.

Задачи: — Владеть продуктовой областью от идеи до доставки и определять развитие дорожной карты — Закрывать разрыв между тем, что продукт сообщает, и действиями клиента: решать, что рекомендовать и кому, и строить цикл улучшения рекомендаций — Работать с AI от макетов и прототипов до продакшн-кода для небольших функций вместе с инженерной командой из 3-10 человек — Проводить самостоятельное исследование клиентов и превращать результаты в решения по дорожной карте — Определять и измерять метрики, доказывающие, что продукт вызвал действие — Создавать и поддерживать автоматизированные рабочие процессы с использованием AI-инструментов, скриптов и современного дата-стека — Сотрудничать с командами Product, BI и CSM по архитектурным решениям и требованиям к данным клиентов Требования: — Опыт владения продуктом: способность вести проблему от начала до конца с минимальным контролем и наличие построенных продуктов, которые используются — Практический опыт использования AI-инструментов в работе: Claude Code, агентное кодирование, prompt engineering с пониманием, что не делегировать — Опыт создания AI-агента и обеспечения его надежности в продакшене — Опыт работы с маркетинговыми или доходными данными, умение самостоятельно рассчитывать метрики — Умение работать с инженерами на равных, а не через передачу задач — Хорошее знание SQL — Python для обработки данных и автоматизации — Свободный английский язык для общения с клиентами и эскалаций — Готовность пересекаться с рабочими часами US Pacific или East Желательно: — Опыт работы с ETL/ELT пайплайнами (BigQuery, Snowflake, Redshift, ClickHouse) — Опыт работы напрямую с клиентами по техническим требованиям — Опыт работы с BI-инструментами (например, Superset) Условия: — Удалённая работа в первую очередь — Опциональная помощь с релокацией в Латинскую Америку после испытательного срока — Современный AI-стек и свобода экспериментов — Высокая скорость работы и постоянное обучение — Высокая автономия и ответственность за результат — Культура без бюрократии с технически сильной международной командой — 20 рабочих дней отпуска в году, праздники США и дополнительные выходные — Компенсация профессионального развития

Location: Distributed; Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin TX, San Francisco CA, NYC, Atlanta GA OR Denver CO About the role The GTM Intelligence team helps Cloudflare's go-to-market organization make faster, better decisions with trusted data. We work with Sales, Technical Sales, Services, Finance, and our central Data teams to make sure the people who sell, deploy, and support Cloudflare get consistent, reliable answers to the questions that matter most. We're looking for a Senior GTM Analytics Manager to set and execute the vision for how information scales across Cloudflare's technical customer-facing teams. You'll support Pre- and Post-Sales Operations, Solutions Engineering, Forward Deployed Engineering, and Professional Services. This role is less about building more dashboards or doing one-off deep dives and more about building the foundations to support true self-serve analytics.  You will focus on single source of truth artifacts, strategic alignment of metrics to actions, and the data models, metric definitions, and context layers that let anyone in these organizations get a trustworthy answer that fits their needs. That person might be an executive, a frontline manager, an engineer in the field, or an AI agent working for any of them. You'll be a technical expert and a strategic partner in one. You'll be as comfortable reviewing a dimensional model with Data Engineering as you are agreeing a metric definition with a VP of Professional Services. And you'll be our go-to voice on how analytics should change now that more and more questions get answered through AI. This is a senior individual contributor role with wide influence and no direct reports. What you'll do Define and drive a multi-year strategy for how Pre-Sales, Post-Sales, Solutions Engineering, FDE, and Professional Services find, trust, and use data, including a maturity model and a phased roadmap. Act as a thought leader on scaling information in an AI-first world, working out how semantic layers, governed metrics, and well-built context let AI assistants and agents give accurate, consistent answers grounded in our business. Review the current mix of dashboards, reports, and tools across these teams. Create a plan to consolidate them, retire duplicates, and move toward a single source of truth. Define, document, and carry out the official metrics for the teams you support. Keep metric definitions aligned with corporate definitions from Finance and Data, and resolve conflicts between teams with clear, defensible logic. Act as the main business and technical partner to the central Data team for the stakeholders you support. Influence and review the design of data models across systems such as Salesforce, professional services tools, product usage and telemetry, support, and finance. Design how people get answers: self-serve access, natural-language and AI-assisted querying, embedded insights in daily workflows, and curated metric catalogs. Set up a formal intake and governance process for new metric and data requests, including prioritization, ownership, certification, and change management. Build trusted relationships with leaders in Sales, Solutions Engineering, FDE, Professional Services, Customer Success, Operations, Finance, and Data. Explain complex technical and analytical ideas clearly to executives so they can make decisions. Desirable Skills, Knowledge, and Experience: 8+ years in analytics, business intelligence, analytics engineering, or GTM/Services operations at a high-growth B2B SaaS or technology company. Direct experience supporting at least one of the following: Pre- or Post-Sales Operations, Solutions/Sales Engineering, Forward Deployed Engineering, Professional Services, or Customer Success. You understand how these teams work, how they're measured, and how their data is created. Deep data modeling expertise, including dimensional modeling, entity design, and metric layer design. You can review and challenge a data model, not just consume one. Expert SQL skills and hands-on experience with modern data stack tools such as BigQuery, dbt, or a semantic/metrics layer (for example dbt Semantic Layer, LookML, or Cube). Strong working knowledge of GTM and services systems data, such as Salesforce, professional services automation tools, and product usage or telemetry data. A clear, informed view on how AI changes analytics, including semantic layers, retrieval and context design for LLMs, text-to-SQL, and agent-based access to governed data, plus the data foundations those need to be accurate. A track record of defining and rolling out source-of-truth metrics across multiple stakeholder groups, including handling disagreements over definitions. Proven ability to lead complex, cross-functional programs without formal authority, and to influence executives and engineers alike. Excellent written and verbal communication skills, including turning ambiguous business questions into precise technical requirements and back again. BA/BS in a quantitative, technical, or business field. A Master's in Data Science, Analytics, Computer Science, or an MBA is a plus. Compensation Compensation may be adjusted depending on work location. Compensation may be adjusted depending on work location. For San Francisco Bay Area based hires: Estimated annual salary of $167,000 - $230,000. For New York City based hires: Estimated annual salary of $160,000 - $220,000. For Colorado - Remote based hires: Estimated annual salary of $146,000 - $200,000. Application Deadline: ~January 2027 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: ES - Barcelona, Spain Senior Data Platform Engineer Scopely is looking for a Senior Data Platform Engineer to join our Playgami Data Platform team. At Scopely, we care deeply about what we do and want to inspire play every day - whether in our work environments alongside our talented colleagues or through our deep connections with our communities of players. We are a global team of game lovers who are developing, publishing, and innovating the mobile games industry, connecting millions of people worldwide daily. Playgami builds technology for game teams across Scopely. Our Data Platform provides the shared foundation that enables teams to use data safely and reliably at scale, supporting 15+ games and 2,000+ active users across Scopely and processing more than 60TB of data per day . What You’ll Do As a Senior Data Platform Engineer, you will build and improve the shared infrastructure, tooling, and services that power data and analytics teams across Scopely. You will investigate platform needs, evaluate technical tradeoffs, and turn agreed priorities into tested code and reusable infrastructure. Working closely with data and analytics teams, Security, and Governance, you will take platform improvements from technical design through deployment and operation. Your main responsibility will be to improve the security, reliability, scalability, and efficiency of our Data Platform while making it easier for teams to build and operate their data workloads. You will lead the technical design and delivery of platform improvements, making informed tradeoffs between reliability, security, scalability, and cost as part of a shared team roadmap. Build and operate shared GCP and BigQuery infrastructure using Terraform, automated checks, and CI/CD. Build scalable, self-service platform capabilities that enable data teams to develop, deploy, and operate workloads with less manual support. Improve user and service account access, ensuring least-privilege principles and dependable approval, review, and revocation processes. Translate Security and Governance requirements into practical controls for sensitive data and data sharing. Improve Airflow and Cloud Composer reliability through troubleshooting, staged upgrades, and tested recovery processes. Improve cloud data platform performance and cost efficiency using workload and capacity insights. Build actionable monitoring and alerts, investigate incidents, and implement improvements to prevent recurring failures. Partner with teams to understand their needs, investigate platform challenges, evaluate potential solutions, and help shape the technical roadmap. Make platform workflows easier to adopt and operate through documentation, runbooks, and mentoring. Provide infrastructure, access controls, and shared tooling that help teams use data safely and reliably at scale. What We’re Looking For Experience designing, deploying, and operating production cloud or data platforms, including safe releases and recovery. Hands-on experience with GCP, BigQuery, IAM, service accounts, and Cloud Storage , or strong equivalent cloud experience. Experience authoring Terraform and CI/CD workflows , testing infrastructure changes, and resolving drift. Experience with Python for reusable automation and SQL for investigating data and workload behavior. Experience implementing and validating least-privilege access, sensitive data controls, and audit logging. Experience operating Airflow, Cloud Composer, Dagster , or a similar workflow orchestrator. Demonstrated experience reducing manual work or improving platform reliability, workload performance, or cloud costs. Strong English communication skills and experience collaborating with engineering, security, and data teams. Bonus Points Experience with BigQuery reservations, capacity management, query optimization, and cost attribution. Experience with Cloud Composer migrations or upgrades, multi-environment orchestration, and recovery testing. Experience with Workload Identity Federation, short-lived credentials, access approval integrations, or tools such as Britive and Lumos. Experience with data classification, column-level controls, masking, controlled sharing, or automated discovery of sensitive data. Experience automating and operating Looker, dbt, Fivetran, or Cube as shared platform services. Experience supporting automated data workloads, including access controls, monitoring, and cost limits. Experience using AI-assisted tools for platform automation, troubleshooting, or documentation, with appropriate testing and review. You do not need experience with every tool listed. We value production ownership, sound engineering judgement, and the ability to learn. Relevant experience can come from data platform, cloud infrastructure, software engineering, DevOps, or site reliability roles. About Scopely Scopely is a leading video game and global interactive entertainment company, home to many of the world’s most beloved and enduring experiences, including two of the most successful mobile games of all-time “MONOPOLY GO!” and “Pokémon GO,” along with “Stumble Guys,” “Star Trek™ Fleet Command,” “MARVEL Strike Force,” “WWE Champions,” the Scrabble® franchise, “Yahtzee® With Buddies,” and many others. Across mobile, web, PC, and console, Scopely creates, develops, publishes, and live-operates one of the most diversified and award-winning portfolios in the games industry — bringing hundreds of millions of players together through a shared love of play. Founded in 2011, Scopely is powered by its exceptional team — including thousands of world-class gamemakers around the globe, a distinctive tenet-driven culture, and its proprietary technology platform, Playgami. Together, these strengths have fueled Scopely’s position as the #1 mobile games company in the U.S. and #2 globally, generating more than $10 billion in lifetime revenue. Whether building global sensations like “MONOPOLY GO!” from the ground up, or expanding through strategic acquisitions, including the FoxNext, GSN, and Scopely Explore games businesses — Scopely consistently delivers experiences players love today and return to for years to come. Recognized multiple times as one of the "100 Most Influential Companies in the World" by TIME magazine and one of Fast Company's "World's Most Innovative Companies" and “Best Workplaces for Innovators,” Scopely believes that video games can be a force for good — creating meaningful connections, vibrant communities, and making life better through play. Scopely has global operations and partners across four continents in more than a dozen countries worldwide. For more information, visit: https://www.scopely.com/ . Notice to Candidates: Scopely will never request payment or financial information during the application or hiring process. Please apply only through our official website and verify that all Talent Partner communications come from an email address ending in @ scopely.com . Should you have any questions or encounter any fraudulent requests/emails/websites, please immediately contact recruiting@scopely.com. Our job applicant privacy policies are available here: California Privacy Notice and EEA/UK Privacy Notice . Employment at Scopely is based solely on a person's merit and qualifications. Scopely does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law. We also consider qualified applicants with arrest or conviction records, consistent with applicable federal, state and local law.

Location: Amsterdam, Netherlands At Catawiki, every day brings the extraordinary! Whether it’s Daniel Ricciardo’s Formula 1 Car , a Woolly Mammoth’s Skeleton , Lady Gaga's Jumpsuit or Usain Bolt’s running shoe , we encounter exceptional objects every day. We’re a one-of-a-kind marketplace for buying and selling special objects. Each week, more than 100,000 unique items are auctioned, all carefully curated by our passionate in-house experts. Having sold over 25 million unique objects, our mission is to become the world’s most popular destination for special objects. As a growing, diverse and sustainable scale-up, we proudly live by three core values. If these values resonate with you, we’d love to explore how you can join us. Taking ownership and driving impact Being open to change and feedback Being passionate about our mission and our customers. Your mission As Senior Manager – Data Engineering & Machine Learning, you’ll lead Catawiki’s established team of Data Engineers and Machine Learning specialists. You’ll help the team grow, set its technical direction and build on work already underway across data foundations and production machine learning. The role spans two connected disciplines. You’ll bring deep experience in either Data Engineering or Machine Learning Engineering, and enough breadth to set direction across the other. You’ll partner with Product, Engineering, Analytics and business stakeholders to set priorities and connect the team’s work to outcomes across Catawiki. What you'll do You’ll lead the team through platform modernisation and product-facing ML work. Current priorities include modernising the infrastructure for serving production models, introducing a feature store to improve feature management and consistency across model training and serving, and strengthening observability and cost controls. You’ll help set priorities and raise the reliability and impact of this work. Lead and develop the team: Set direction, coach experienced specialists and create clear ownership across Data Engineering and Machine Learning. Build dependable data foundations: Improve the pipelines, integrations and infrastructure the wider Data team relies on for analysis, reporting and machine learning. Strengthen production ML: Improve deployment, monitoring and operational follow-through for production models, so teams can run them reliably and resolve issues effectively. Advance marketplace models: Help teams improve recommendation, cancellation and bidder-risk use cases, connecting model performance to meaningful product outcomes. Set direction and priorities: Work with Product, Engineering, Analytics and business partners to choose high-value problems and balance delivery, reliability and long-term platform health. Shape the technology strategy: Reporting to the SVP Engineering, you’ll join the Engineering Management team and help shape the direction of the Technology department. What you'll bring You combine strong technical judgement with experienced people leadership. You create clarity in a complex environment, empower specialists and make sound decisions across both disciplines. Your experience will include the following. Familiarity with Catawiki’s current stack is useful, and equivalent experience with other platforms is welcome. Technical depth in one discipline: Deep experience designing, building and operating data-intensive systems in Data Engineering or Machine Learning Engineering, with practical knowledge of the other. People leadership: Experience leading experienced engineers or ML specialists, coaching their growth and setting clear expectations. Architecture and systems thinking: Ability to evaluate trade-offs and guide architecture for data platforms, distributed systems and production infrastructure. Production ML understanding: Experience with the deployment, monitoring and operation of machine learning systems in production. Business and product judgement: Ability to connect technical work to business goals, make priority calls and assess outcomes. Relevant cloud platform experience: We run on GCP using BigQuery, Vertex AI and Kubernetes. Experience with equivalent AWS services is also relevant. Streaming and integration experience: Kafka, event-driven systems or modern data integration tools are useful. Clear communication: Ability to explain technical trade-offs and align technical and non-technical stakeholders. How we work You’ll lead a team of eight Data Engineers and Machine Learning specialists across Amsterdam and Lisbon. The team works closely with Product, Engineering, Analytics and business stakeholders to connect data and ML capabilities to real needs. Specialists own implementation while you set direction, priorities and clear standards. The team supports production data and ML systems, and has no on-call rotation. Where you'll be This is a hybrid role in Amsterdam. You will work from our Amsterdam HQ a minimum of 2 days per week. Why You'll Love Working with Us Create a visible impact by working at scale in a global organisation serving millions of customers across 80+ categories. In our flat structure, every role has a broad scope and directly impacts both our customers and the business. Learn and grow through our Learning & Development initiatives, including clear development plans and mentorship programmes to support your career progression. A culture of connection defines us . We’re a passionate, diverse team of 800+ Catawikians representing 60+ nationalities. We foster an inclusive and queer-friendly environment where everyone is encouraged to bring their full self to work. Celebrate life’s moments with us. You’ll receive a €100 Catavoucher when you join, a €50 Catavoucher on your birthday, and an extra day off each year to “Pursue Your Passion ” . We also offer additional leave for key work anniversaries and important life events. Benefits may vary by location. Our Offices and Way of Working Our vibrant offices in Amsterdam and Lisbon are designed to inspire collaboration. Most Catawikians operate in a hybrid setup, combining office-based and remote work, with a minimum of two days per week in the office, unless a role is explicitly stated as fully remote or fully office-based. Interested? Apply with an English CV and Cover Letter. By applying, you agree to Catawiki’s Applicant Privacy Policy. If you’re excited about this role but don’t meet every requirement, we still encourage you to apply anyway. You may be just the right candidate for this or other roles. We may use artificial intelligence (AI) tools to support the review of your application. AI may help us assess how your experience matches the requirements of the role, but hiring decisions are always made by our recruitment team

• Configure and troubleshoot GTM, GA4, and Consent Mode v2. • Implement dataLayer schemas and prepare technical specs for developers.

Design, build and maintain Python services and APIs behind the funnel: registration, booking and the visitor account. Build and own server-side integrations with ad platforms (Meta CAPI, TikTok Events API and similar): event delivery, deduplication, retries and monitoring.

Location: New York, NY; Pittsburgh, PA Our mission at Duolingo is to develop the best education in the world and make it universally available. It’s a big mission, and that’s where you come in! At Duolingo, you’ll join a team that cares about educating our users, experimenting with big ideas, making fact-based decisions, and finding innovative solutions to complex problems. You’ll have limitless learning opportunities and daily collaborations with world-class minds — while doing work that’s both meaningful and fun. Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world. Read our blog to learn more. About the role You will... Derive insights from data to influence product roadmaps and drive business results Apply advanced analytical methods, structural approaches, causal inference, and/or machine learning and AI to model user behavior and estimate the impact of potential new feature implementations, and collaborate with product and engineering teams to develop solutions Evaluate product experiments to create and improve user experiences Communicate and present findings and recommendations to senior management Help preserve our culture of fact-based decision making as we scale You have... Pursuing a PhD degree (in at least the third year of PhD program) in a quantitative field (e.g. Economics, Finance, Statistics, Math, Linguistics, Computer Science, Computational Social Science, ML/AI), ideally planning to graduate in Fall 2027 or Spring/Summer 2028 Strong understanding of consumer digital products and an interest in applying advanced analytics and economics to business outcomes Previous experience analyzing large datasets, communicating results to technical and non-technical senior-level audiences, and making actionable recommendations that improve the product or business Skilled in SQL, R or Python, and/or statistical methods to analyze behavioral data Enthusiasm for learning and sharing analytical methods to help grow a collaborative Data Science team and data-forward company culture Able to relocate to Pittsburgh or NYC for the internship Exceptional candidates will have... Experience with "big data" and cloud computing technologies like BigQuery, Snowflake, and/or dbt Experience articulating business questions and using available data to find answers that translate into product recommendations Self-starter, with a desire to both learn new techniques and guide others Previous experience/internships in applied business settings Familiarity with AI tools such as Cursor, a knack for prompt engineering, and a desire to continue to innovate and learn in AI A GPA of 3.5+ A Duolingo streak Why you’ll love working here… Language learning opportunities Catered in-house lunches and breakfast Company-wide Hackathon PTO and company holidays Frequent company-wide dinners, monthly celebrations, social clubs, and so much more! Connect with your recruiter on the benefits for interns at Duolingo! Hourly Range for this internship is: $63 — $65 USD Benefits: Take a peek at how we care for our employees' holistic well-being with our benefits here . Job Alerts: Sign up for job alerts here . Accommodations: We will do everything we can within reason to make sure that your interview takes place in an environment that fairly and accurately assesses your skills. If you need assistance or accommodation, please contact accommodations@duolingo.com . Equal Employment Opportunity: Duolingo is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Fraud Warning: Unfortunately, there is a rise in scammers pretending to be real Duolingo employees. Duolingo and our employees will never ask for your Social Security number, bank details, or passport info, and we’ll never ask you to deposit a check, purchase equipment, or exchange money during the interview process. Real Duolingo employees always use an email that ends in @duolingo.com or @recruiting.duolingo.com. Stay alert and double-check these details before sharing any information. By applying for this position your data will be processed as per the Duolingo Applicant Privacy Notice .

As the Principal Product Manager for ClickHouse Cloud, you will be responsible for leading the development and delivery of the product working closely with engineering, customers, and stakeholders. What you will do:

Location: Minsk Workplace: hybrid About NRS National Retail Solutions (NRS) is a leading provider of point-of-sale, payment, and digital solutions to tens of thousands of independent retailers across the United States and Canada. As a major player in the retail technology industry, NRS is expanding into next-generation cloud platforms, high-throughput retail ecosystems, and advanced analytics solutions. We are building modern, highly scalable systems designed specifically for high-volume retail operations and real-time reporting. This project represents one of the most significant technical initiatives in NRS’s history and offers a rare opportunity to join at the ground floor of a major strategic expansion. Role Overview: We are seeking a Senior Full Stack Engineer based in Minsk, Belarus to join our distributed engineering team. In this role, you will help architect and build core components of NRS’s next-generation retail platform. You will focus heavily on robust backend services written in Golang, high-throughput query performance, and leveraging ClickHouse for real-time reporting and large-scale data modeling, alongside modern front-end architectures using Vue.js and micro-frontends Experience with columnar/OLAP databases like ClickHouse is highly desired to support our analytical engine, but deep, hands-on mastery of SQL, data indexing, and database performance profiling is the primary requirement. You will work closely with engineering leadership, product managers, and architects across Israel, Belarus, and the U.S., contributing meaningfully to the technical direction, data architecture, and scalability of our products. This position requires 3 days a week in-office work at our office in Minsk to collaborate in person with local engineering peers, along with regular remote collaboration across our global teams Responsibilities: Technical Leadership & Architecture Architect, build, and maintain high-performance full-stack applications—ranging from real-time analytics dashboards to reporting pipelines—with a strong focus on Golang for backend microservices and Vue.js on the front end. Design, model, and optimize high-speed relational and analytical data stores—writing advanced SQL queries, materialized views, aggregation logic, and indexing strategies to power real-time reporting dashboards. Leverage analytical data stores (ideally ClickHouse ) to build ultra-efficient reporting engines and handle high-throughput data ingestion. Design and implement modular micro-frontend architectures to support scalable, independently deployable UI components across team boundaries. Take end-to-end ownership of core system components, high-throughput backend APIs, and event-driven microservices. Drive architectural design, technical planning, query optimization, and engineering best practices across the team. Ensure platform scalability, low latency, fault tolerance, and secure service design. Execution & Delivery Write clean, maintainable, highly efficient, and well-tested Go and JavaScript/TypeScript code. Collaborate with product and engineering teams in the U.S. and locally to translate complex requirements into robust, scalable technical solutions. Uphold code quality through thorough code reviews; we use AI-assisted tooling (Cursor, Copilot, Claude Code) as part of our workflow. Debug, profile, and optimize complex SQL queries, execution plans, database locks, and distributed backend microservices. Collaboration & Culture Participate in daily standups, sprint planning, and retrospectives to support continuous delivery. Communicate effectively across time zones and collaborate seamlessly with global engineering leadership. Required Qualifications: 6+ years of full-stack development experience , with at least 2 years in a senior or lead capacity . Solid Backend Expertise in Go: Proven track record of designing, building, and scaling Go-based microservices, concurrent applications, and REST/gRPC APIs. Deep SQL Mastery & Data Modeling: Expert-level knowledge of SQL, including writing complex joins, subqueries, query execution plan analysis (EXPLAIN), indexing strategies, schema normalization/denormalization, and performance profiling for large-scale reporting datasets. Modern Frontend & Micro-Frontend Mastery: Strong hands-on experience with modern JavaScript/TypeScript frameworks—specifically Vue.js (Vue 3 / Composition API). Demonstrated experience designing or working within micro-frontend architectures (e.g., Module Federation, single-spa, component isolation). Experience building end-to-end SaaS products, microservices, or complex distributed web platforms. Proficiency with Linux/Unix environments, Git, and CI/CD automation workflows. Strong problem-solving mindset with the ability to lead technical initiatives independently. Excellent English communication skills with experience collaborating with remote/U.S.-based stakeholders. Nice to Have: ClickHouse / OLAP Experience: Direct hands-on experience with ClickHouse or other columnar/analytical databases (Snowflake, BigQuery, Redshift) is a strong plus . Working knowledge of PHP (legacy service integration/migration). Familiarity with containerization and orchestration ( Docker, Kubernetes ). Hands-on experience with cloud platforms (ideally AWS ). Knowledge of real-time communication technologies ( WebSockets, gRPC, Kafka, or NATS ). We are pleased to offer: Hybrid work opportunity (3 days in the office)! Modern office with free parking in the very center of Minsk. An exciting and challenging job with talented people around. Competitive salary and growth opportunities. Really good hardware. 26 vacation days & 5 fully covered sick days without sick leave required per year. Compensation for professional training, seminars, and conferences. Sports activities reimbursement and loyalty program. Medical insurance. Referral program - get rewarded for helping us grow the team with talented people. Company-supported English classes to enhance your professional growth. IDT Corporation is a global communications company founded in 1990 and headquartered in Newark, New Jersey. We are industry leaders in prepaid communication and payment services and one of the largest international voice carriers. We are listed on the NYSE, employ over 1800 people across 20 countries, and  have over $1.5 billion in revenues.     We are not ”another big IT corporation”— we encourage and support in-house entrepreneurs in developing their ideas into business actions.   Our National Retail Solutions brand provides a sales management system and POS equipment to small and medium-sized businesses. With the help of NRS, business owners can solve a management problem: the product includes integrated advertising, data analysis, and payment processing.

Задачи: — Разрабатывать безопасные платежные сервисы на C# / .NET 8+ — Проектировать процессы выпуска виртуальных карт, маршрутизации, работы с кошельками и бухгалтерскими проводками — Обеспечивать идемпотентность, согласованность и ведение аудита — Интегрироваться с платежными сетями, PSP и системами отелей — Соблюдать стандарты PCI DSS, PSD2 / SCA и SOC 2 Требования: — Опыт работы 5+ лет с C# / .NET (недавний .NET 8+) — Опыт 2+ года в платежных системах или финтехе — Сильные навыки обеспечения транзакционной согласованности и распределённых систем — Опыт работы с GCP, включая Spanner, BigQuery или PostgreSQL — Знания по виртуальным картам Visa / Mastercard и валютным операциям будут плюсом Условия: — Полностью удалённая работа в международной команде — Работа с реальными деньгами и множеством валют — Высокая степень ответственности и безопасность в центре внимания