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About the project: A fast-growing fintech startup providing SaaS solutions for institutional investors. The platform includes advanced solutions for financial analysis, trade flow management and portfolio management. Requirements: — Strong experience with C# / .NET / ASP.NET Core — Solid experience with Angular — Good understanding of SOLID principles and design patterns — Working knowledge of relational databases — Familiarity with Git — Good understanding of UI/UX principles — Experience working in an Agile environment — Strong communication and collaboration skills What we're looking for: — A self-driven engineer passionate about technology — Someone who takes ownership and accountability for their work — Strong attention to detail and a focus on quality — A proactive approach to automation and process improvement — Ability to clearly communicate technical ideas — Comfortable working in a fast-paced, highly collaborative environment — Open to feedback and able to work effectively with both technical and business stakeholders Responsibilities: — Design, develop and maintain components and frameworks for the client's technology ecosystem — Collaborate with internal and external stakeholders to understand application development needs — Research and evaluate new technologies and use cases — Identify inefficiencies and implement appropriate technology solutions — Contribute ideas and improvements to the product and development process

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD Most subscription businesses lose a password. We lose GPU-seconds. When someone runs two hundred accounts off one card to harvest a per-account promotional grant, the cost lands on our compute bill the same hour, and it does not stop until somebody finds them. We have found operators doing exactly that — hundreds of accounts, synchronised signups, machine- generated emails, round-the-clock automation — and we have also found that most of our worst- margin accounts are not abusers at all, just heavy users on an unlimited plan. Telling those two apart, account by account, with evidence that survives being challenged, is this job. You are the person who finds them, proves it, and puts a number on it. You make sure: - Every enforcement action we take is backed by evidence a stranger could follow - We know what abuse cost us last month, on the same basis as last month - The detection system is measured against real cases, not against its own past decisions - When a ring changes shape, somebody notices this week — and that somebody is you WHAT YOU WILL DO Investigation & casework - Run ring investigations end to end: from a flagged account or a cost anomaly to a named cluster, its members, its signature, its lifetime economics and a recommended action. - Build the linkage yourself — shared payment instruments, IP and ASN concentration, email-stem families, registration bursts, behavioural and automation fingerprints — and know which links are evidence and which are coincidence. - Separate abuse from unprofitable-but-legitimate every single time. On an unlimited plan, negative gross margin is normal. Cost is a gate, never a trigger. - Triage the review queue the detection system produces, and feed what you learn back into it. Your adjudications are the only unbiased labels the scientist has. Evidence & defensibility - Produce evidence packages that hold up outside the company — to an external auditor, to a payment network, to a customer disputing a ban, to Legal enforcing our Terms. Our abuse work has already been used in all four contexts. - Write per-account, not per-ring, when the action is per-account. A ban list without a reason column is not evidence. - Know what our Terms of Use actually entitle us to do, and flag when the evidence supports the finding but not the action. Quantification & reporting - Own the abuse loss number: what it cost, on what basis, net of the revenue we reversed and the legitimate customers we caught by mistake. Same definition every month, written down. - Distinguish COGS burned, revenue at risk, revenue reversed and cash refunded — they are four different numbers and people will conflate them. - Build the recurring reporting that tells us whether the problem is growing, and the ad-hoc answer to "why did compute spike last Tuesday" that is due the same day. Monitoring & adaptation - Watch the detectors themselves. A rule that stops firing is a detection event, not a quiet week. - Check the instrument before explaining the movement: a missing upstream table, a silently dropped join, a vendor signal that went away. This has bitten us and it will again. - Spot new abuse patterns before they are in anybody's model — usually in a cost anomaly, a strange cohort, or a support ticket that does not fit. How we work - SQL and Python on raw payment, usage and identity data in BigQuery, without anyone preparing it for you. We use AI heavily — resourcefulness beats syntax. - Close partnership with the Antifraud data scientist (you validate what they build), Payments (chargebacks and processor signals), Support (the humans who meet your false positives) and Finance (the loss number goes in the accounts). WHO WE'RE LOOKING FOR - 2+ years doing investigative or risk analytics — fraud, abuse, trust and safety, AML, chargebacks, gaming or marketplace integrity. What matters is that you have chased real bad actors, not modelled them in the abstract. - Strong SQL, without help. Self-joins, window functions, cohort replay, and joining payments to usage to identity across messy keys. This is the single most-used skill in the role. - Python at working level for analysis — pandas, notebooks, a bit of graph work. - Investigative instinct: you follow the thread, you notice the thing that does not fit, and you can say when a pattern is a coincidence. - Evidentiary discipline: you can write up a finding so that somebody who distrusts you can check it, and you know the difference between "this looks like abuse" and "this is provable." - Unit-economics literacy: gross margin per account, what compute costs, and why a negative-margin customer may be entirely legitimate. - The judgment to escalate rather than act when the evidence is thin, and to say so in writing. - Clear written and spoken English, B2+. Backgrounds that often do well: - Fraud / risk analysts from fintech, payments, marketplaces, gaming, crypto or betting - Trust & safety investigators and platform-integrity analysts - Chargeback, dispute or AML analysts who write their own queries - Strong product or BI analysts who got handed the abuse problem and kept it WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to build models full time — that is the Antifraud data scientist, and it is open - Want to do content moderation or NSFW classification - Would put an account on a ban list without being able to say why in one sentence - Treat every negative-margin account as an abuser - Need a labelled dataset or a clean table before you can start - Are uncomfortable that your work gets real people's accounts restricted, sometimes wrongly - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1,5 hour) - Tech interview (1,5 hour) - Final interview (30 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD Generative video has a problem most subscription businesses don't: every unit we sell costs us real compute. A stolen password costs a streaming service nothing. An account farm on an unlimited generation plan costs us GPU-seconds, at scale, every hour it runs. We have found organised operators running hundreds of accounts on a single card, reselling unlimited generations, and harvesting per-account promotional grants at industrial scale. We have also found that most of our negative-margin accounts are not abuse at all — they are ordinary heavy users on an unlimited plan, which is a pricing problem wearing a fraud costume. Telling those two populations apart, at account level, before the money is gone, is this job. You own the question of who is taking value they did not pay for, and the system that stops them. You make sure: - We catch abuse before the compute is burned, not in a post-mortem that reconciles the loss - Every enforcement action has a measured precision, a measured cost of being wrong, and a way back for the customer we got wrong - The difference between fraud and unprofitable pricing is a number we can defend, not a vibe - When operators adapt — and they will, within days — we find out from a monitor, not from the monthly margin review WHAT YOU WILL DO Detection & scoring - Own the account-abuse scoring system end to end: signals, weights, thresholds, the scoring job, the monitoring, and the recalibration when it decays. - Build detection that fires early. A rule that catches 88% of the money on day 11 is worth far less than one that catches 60% at checkout, and you should be able to say why in dollars. - Work the two halves that actually matter together: linkage (who is connected to whom) and economics (what are they consuming). Either one alone produces a system nobody can switch on — linkage without economics flags families sharing a card, economics without linkage flags our best customers. - Treat cost as a gate, not a label. On an unlimited plan, being gross-margin negative is normal. Entity resolution & ring detection - Find rings, not just accounts: shared payment instruments, IP and ASN concentration, email-stem families, synchronised registration bursts, behavioural fingerprints, automation signatures. - Do it without a reliable device fingerprint — we do not have one today, and part of this job is telling us what it would be worth and what it would cost. - Know where graph methods break. Transitive closure through a shared card will happily merge thousands of unrelated people into one "ring"; we have done exactly that and thrown the result away. Enforcement, policy & the cost of being wrong - Design graduated, reversible actions — throttle, rate-limit, step-up verification, hold, pre-grant refusal, manual review queue, ban — and match the severity to the confidence. - Own the false-positive budget explicitly: how many paying customers are we willing to inconvenience to save a dollar of compute, and what is the appeal path for the ones we get wrong. - Build the pre-grant gates. The cheapest abuse to stop is the kind we decline to enable in the first place — a trial we do not grant costs nothing to claw back. - Partner with Payments on Stripe Radar, chargebacks and dispute economics; with Support on the queue your model creates; with Legal on what our Terms actually entitle us to do. Measurement & adversarial monitoring - Quantify what the system is worth in money — COGS avoided, net of the revenue you destroyed and the support cost you created. Keep that number current. - Run holdouts even on enforcement, wherever it is legal and safe to do so, because otherwise you will never know what your rules are worth. - Monitor for adaptation, not just drift. Model decay here is caused by an adversary, and it looks different: a signal stops firing, a ring changes shape, a cohort's behaviour shifts inside a week. - Watch your own inputs. A detection system that silently loses a feature degrades to a system that cannot reach its own threshold — and reports nothing wrong while doing it. Making it usable - Ship scores and reasons, never scores alone. An analyst, a support agent or an auditor must be able to read why an account was actioned. - Produce evidence that survives outside the company. Our abuse work has already been used in an external audit and in Terms-of-Use enforcement; write for that standard. - Work in SQL, Python and BigQuery on raw event, payment and generation data. We use AI heavily — resourcefulness beats syntax. WHO WE'RE LOOKING FOR - Experience building abuse, fraud or trust-and-safety detection that actually took action on real users — payments fraud, promo abuse, multi-accounting, bonus abuse, account takeover, marketplace or gaming abuse. Anti-money-laundering and credit-risk backgrounds transfer if the modelling was yours. - Genuine understanding of the adversarial setting: that your counterparty responds, that your training labels are generated by your own past decisions, and that offline AUC on a censored label set is close to meaningless. - Strong SQL and Python on raw event data without help: joins across payments, usage and identity, window functions, cohort replay. Gradient boosting, and the judgment to know when a two-condition rule beats a model. - Comfort with graph and linkage thinking — connected components, shared-attribute clustering, and an instinct for when a link is evidence and when it is coincidence. - Precision-first evaluation instincts: precision at the operating point, cost-weighted error, and why you would take 20% recall at 90% precision over the reverse in an enforcement system. - Unit-economics literacy: gross margin per account, what compute actually costs us, why a negative-margin customer may be entirely legitimate. - The judgment to escalate rather than act when the evidence is thin, and to say so in writing. - Clear written and spoken English, B2+. Backgrounds that often do well: - Fraud / risk data scientists from fintech, payments, marketplaces, gaming or crypto - Trust & safety and platform-integrity teams who owned detection and enforcement policy - Growth or product data scientists who ended up owning promo-abuse because nobody else would - Analysts from payment processors, PSPs or chargeback/dispute operations who moved into modelling WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to build content-moderation or NSFW classification — that is a different function here - Would ship a ban list without knowing its precision - Optimize AUC on a label set your own enforcement created, and call it validated - Need a labelled fraud dataset to exist before you can start - Treat every negative-margin account as an abuser - Are uncomfortable that being right 90% of the time means being wrong about real, paying, legitimate people — and that you own what happens to them - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - Take-home with real-shaped data (7 days, ~10–12 hours of work) - Team interview (60 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD We sell subscriptions and credit packs to consumers in over a hundred countries, and today almost all of it moves as cross-border card payments in USD. That has three consequences, and this role owns all three. We are declined more than we should be. In some of our largest growth markets roughly one attempt in three fails, and a meaningful share of those are not "no money" — they are cross-border blocks, card limits, stale credentials on renewal, and issuers who do not recognise us. We pay more than we should. Our blended cost of acceptance is the single largest line below compute, and the majority of the network component sits on transactions our processor classes as cross-border. We are not on the rails our customers use. In two of our fastest-growing markets, the dominant local payment method is one we do not offer at all. You own the measurement and the argument behind fixing all of it. You make sure: - We know our true authorization rate — at the unit the business actually cares about, not the one that is easy to compute - Every basis point of payment cost has a name, a geography and an owner - A decline is a diagnosis, not a statistic - When we propose a new rail, a new entity or a new processor arrangement, the number underneath it holds up to Stripe's own analysis and to our board's WHAT YOU WILL DO Authorization & decline analysis - Own authorization rate end to end: by country, issuer, BIN, card brand and funding type, plan price point, first payment vs renewal, and payment method. - Build and maintain the decline taxonomy — what each decline code actually means, which are recoverable, which are terminal, and which are us rather than them. - Measure at the right unit. An invoice that succeeds on the third retry is a success, not two failures and a success, and reporting it the other way has made teams optimise the wrong thing. - Separate soft declines we can recover from hard declines we cannot, and put a number on the recoverable pool. Cost of acceptance - Own the blended take rate and its decomposition: interchange, scheme fees, processor markup, cross-border and currency-conversion components, disputes, and the fixed per-transaction pieces. - Find where the cost actually sits. Averages hide it — the cost is concentrated by corridor, by card type and by whether the transaction is domestic. - Build the evidence for commercial conversations with our processor, and keep it current enough to reuse. Local payment methods, entities and routing - Size the case for new rails market by market: what share of local commerce runs on them, what our current card performance costs us, and what we would realistically recover. - Model merchant-of-record and local-acquiring arrangements against the alternative of our own entities, honestly — including the fee we would pay for the privilege. - Design and read the experiments that prove it, with the product analytics team. A new payment method is an A/B test, not a launch. Renewals, dunning and involuntary churn - Own the retry and dunning strategy as a measurable system: when to retry, how often, through which rail, and when to stop. - Quantify involuntary churn separately from voluntary. They look identical in a cancellation count and they need completely different fixes. - Track the health of stored credentials — network tokens, card-account-updater coverage, expiring cards — because in a subscription business that is silent revenue leakage. Disputes, refunds and reconciliation - Own dispute and chargeback rates against network thresholds, with the lag handled properly — disputes arrive weeks after the charge and a same-period denominator understates you. - Reconcile processor data to the revenue ledger and explain the differences rather than plugging them. - Work with the antifraud team where disputes are abuse rather than genuine dissatisfaction. HOW WE WORK - SQL and Python on raw processor and billing data in BigQuery. We use AI heavily — resourcefulness beats syntax. - Partner with Finance (the numbers get booked), Product (checkout and paywall), Engineering (the billing integration) and our processor's team directly. WHO WE'RE LOOKING FOR - 2+ years in payments analytics, payment operations, billing, or risk at a merchant, PSP, acquirer, or fintech. This is a domain role — general analytics experience without payments does not transfer quickly enough. - Fluency in the actual mechanics: authorization vs capture vs settlement, decline codes, 3DS and SCA, network tokens and account updater, interchange and scheme fees, chargeback lifecycle, MCC, BIN, cross-border and DCC. - Strong SQL, without help. Most of this job is joining sessions to intents to charges to invoices to refunds across imperfect keys and getting the denominator right. - Python at working level for analysis. - The instinct to check the unit of measurement before quoting a rate — per attempt, per invoice, per user, per session are four different numbers and only one of them answers the question. - Comfort with subscription billing specifically: renewals, proration, plan changes, failed payments, dunning, involuntary churn. - Commercial judgment: you can build the case for a rail or a rate and defend it against a vendor who does this for a living. - Clear written and spoken English, B2+. Backgrounds that often do well: - Payment operations or payments analytics at a consumer subscription, marketplace or gaming company - Analysts from PSPs, acquirers, orchestration platforms or card networks - Billing / revenue operations analysts who owned dunning and involuntary churn - Risk or chargeback analysts who moved toward the acceptance side WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to own checkout UX or pricing — that is Product Analyst, and it is open - Want to build fraud models — that is the Antifraud data scientist, and it is open - Would report an authorization rate without saying what the denominator was - Would take a processor's own analysis of their own performance at face value - Need someone else to prepare the data - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - Take-home with real-shaped data (7 days, ~10–12 hours of work) - Team interview (60 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Задачи: — Участвовать в формировании продуктовой стратегии data platform — Управлять ожиданиями стейкхолдеров и превращать потребности бизнеса в понятные data products, contracts и delivery plans — People Management: составлять планы развития DE, проводить performance review, 1:1, менторинг, дизайн команды и организация найма при необходимости — Работать со всеми стейколдерами команды (аналитики, dx, security, legal) над развитием data pipelines — Участвовать в развитии архитектуры платформы, модели данных — Управлять техническими решениями в продуктовых командах — Искать и реализовывать меры по оптимизации расходов на storage, compute и pipeline execution — При необходимости подключаться к сложным инициативам как staff data engineer Требования: — Опыт разработки по стеку: Python, Hadoop/Spark, Greenplum/Clickhouse/PostgreSQl, Yandex cloud/GCP/AWS/Azure, Kafka/RabbitMQ — Опыт руководства data engineering или платформенной командой от 5 человек — Понимание data platform architecture, data contracts, lineage, monitoring, reliability — Опыт управления качеством данных и операционными метриками — Сильная коммуникация с бизнесом и смежными командами — Опыт найма, развития и оценки инженеров

Medology, an educational platform for doctors, is seeking a Head of SMM. The role involves creating a comprehensive SMM strategy and defining the roles of various channels such as Telegram, VK, Instagram, YouTube, and TikTok. The candidate will need to build an efficient content system that transforms a single webinar into multiple content formats like Reels, Shorts, interviews, cards, and clips. Responsibilities include attracting and growing a medical audience through seeding, paid advertising, collaborations, and influencer marketing; launching 7–8 specialized Telegram communities in fields like cardiology, neurology, and gastroenterology alongside key medical experts; managing an internal team and contractors; and overseeing end-to-end analytics and KPIs including audience growth, reach, engagement rate, clicks, and registrations. Requirements include at least 2 years of experience in Senior, Lead, or Head of SMM roles; strong expertise in Telegram and viral vertical video mechanics; proven track record of significant channel growth and experience working with niche experts and KOLs; analytical mindset and ability to translate complex scientific and medical information into accessible content; autonomy and leadership skills. Experience in Medicine, MedTech, or Pharma, launching channels from scratch, and having contacts in the medical community are considered strong advantages. The position offers a salary of 150,000 rubles, official employment under Russian labor law, a 5/2 work schedule with a hybrid format combining remote and office work, and an office located near Belorusskaya metro station in central Moscow. The product is strong with a scalable business model.

Описание проекта: создание экосистемной Customer720 платформы. Данные из банковских систем и CRM других бизнесов. Задачи: — Владеть архитектурой и развитием Data Core. — Проектировать слои Bronze / Silver / Gold и модели данных. — Исследовать банковские, legacy- и внешние источники, восстанавливать их семантику. — Строить pipelines от raw-данных до подготовленных data products. — Разрабатывать трансформации на SQL / dbt / Python. — Определять инженерные стандарты для ELT, data modelling, metadata, lineage и data quality. — Развивать data catalog и metadata-as-code подход. — Проектировать data contracts и интеграции Data Core с другими компонентами. — Переводить исследовательские решения и ручные процессы в production. — Работать с Git, CI/CD, Docker, Linux и закрытыми Dev/UAT-контурами. Требования: — Совокупный релевантный опыт не менее 6 лет обязательно. — Банковские или fintech-данные. — Core banking / legacy banking systems. — Опыт работы Lead / Staff / Principal Data Engineer или сильного Senior с архитектурным ownership. — Опыт проектирования и самостоятельной реализации data platform / DWH / Lakehouse. — Customer 360 / Golden Record / MDM. — Сильный SQL и уверенный Python. — dbt или аналогичные transformation frameworks. — Airflow / Dagster / Prefect или аналоги. — Хорошее понимание data modelling, historization, grain и master entities. — Практический опыт data quality, lineage и metadata management. — Data Vault 2.0. — OpenMetadata или другие data catalog solutions. — Умение самостоятельно принимать технические решения и отвечать за результат. — Опыт работы с плохо документированными источниками. — Git, CI/CD, Docker, Linux. — Практический AI-native способ разработки. Условия: — Оформление: ИП, ГПХ. — Формат работы: полная занятость.

Location: New York Workplace: remote ⚡️ About Merkle Science Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate, and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies. Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over $27M from SIG, Beco, Republic, DCG, Kenetic, GGV and several others. Be the primary point of contact and build long-term relationships with customers.  Customer champion within Merkle Science who will work closely with the product team to advocate for key requirements of customers and identify new opportunities. Develop a trusted advisor relationship with key accounts, customer stakeholders and executive sponsors. Ensure the timely and successful delivery of our solutions according to customer needs and objectives. Communicate clearly the progress of monthly/quarterly initiatives to internal and external stakeholders. Forecast and track key account metrics. Enhance department and organization's reputation by accepting ownership for accomplishing new and different requests. Responsible for keeping current clients satisfied and delivering exceptional client service on a day-to-day basis. Monitor and analyze customer's usage of our product. Responsible for working with the Sales team to onboard and integrate new clients and developing existing client relationships. Drive upsells within existing customer base. Fluent in English. At least 3 years of prior account management experience. Previous compliance/web3/blockchain experience. At least 1 prior early-stage startup experience. Proven ability to upsell with exceptionally strong relationship and people management capabilities. A creative problem solver, results driven and self-motivated. Possesses a resilient personality and reacts well under pressure. High emotional intelligence, well organized with the ability to prioritize workload and use time effectively. Ability to handle changing requirements and unforeseen problems rapidly and positively  ❤️ Well Being, Compensation and Benefits We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance.  We regularly host team-building sessions and encourage discussions around mental health.   We reward talent and believe in acknowledging people for their contributions.  We offer industry-leading compensation, along with generous equity.  As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science.

Ищем Manual QA с коммерческим опытом от 1 года в web-продуктах. Задачи: — Frontend, web & mobile testing — Functional, smoke, regression, exploratory testing Требования: — English — B1 Условия: — Будешь работать напрямую с QA, dev, design и Product.

Location: Bangalore Workplace: hybrid ⚡️ About Merkle Science Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies. Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over US$27 MM from SIG, Beco, Republic, DCG, Kenetic, GGV and several others. Be the primary point of contact and build long-term relationships with customers.  Customer champion within Merkle Science who will work closely with the product team to advocate for key requirements of customers and identify new opportunities. Develop a trusted advisor relationship with key accounts, customer stakeholders and executive sponsors. Ensure the timely and successful delivery of our solutions according to customer needs and objectives. Communicate clearly the progress of monthly/quarterly initiatives to internal and external stakeholders. Forecast and track key account metrics. Enhance department and organization's reputation by accepting ownership for accomplishing new and different requests. Responsible for keeping current clients satisfied and delivering exceptional client service on a day-to-day basis. Monitor and analyze customer's usage of our product. Responsible for working with the Sales team to onboard and integrate new clients and developing existing client relationships. Drive upsells within existing customer base. Fluent in English. At least 3 years of prior account management experience. Previous compliance/web3/blockchain experience. At least 1 prior early-stage startup experience. Proven ability to upsell with exceptionally strong relationship and people management capabilities. A creative problem solver, results driven and self-motivated. Possesses a resilient personality and reacts well under pressure. High emotional intelligence, well organized with the ability to prioritize workload and use time effectively. Ability to handle changing requirements and unforeseen problems rapidly and positively  ❤️ Well Being, Compensation and Benefits We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance.  We regularly host team-building sessions and encourage discussions around mental health.   We reward talent and believe in acknowledging people for their contributions.  We offer industry-leading compensation, along with generous equity.  As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science.

Задачи: — Build high-quality software products with attention to detail and craftsmanship. — Work on real-world projects that have measurable impact. — Collaborate closely within an engineering team focused on growth-oriented solutions. — Take ownership from concept through delivery ensuring robustness at every stage. Требования: — Mastery of C# — Strong computer science fundamentals including best practices, design principles, algorithms and data structures. — .NET Core proficiency — JavaScript/TypeScript expertise — Concurrency understanding.

Задачи: — Развитие моделей поискового ранжирования и рекомендательных систем. — Тюнинг моделей и feature engineering. — Разработка новых ML-решений с нуля. — Вывод моделей в production. — Анализ A/B-тестов и результатов экспериментов. Требования: — 6+ лет опыта. — Опыт внедрения ML-моделей в production. — Знание ML и нейросетей для обработки текста. — Опыт в ranking, recommendations или NLP. — Уверенный Python: NumPy, SciPy, PyTorch, Scikit-learn. — Знание алгоритмов и структур данных. — SQL и опыт работы с большими данными. — Понимание A/B-экспериментов. Будет плюсом: — Java. — Cassandra, Hive, Presto, Kafka. — Hadoop. — LightGBM. Условия: — Полная занятость. — График 5/2, 8 часов в день. — Удалённая работа или офис. — Гибкий график. — Оформление по ТК РФ. — «Белая» зарплата. — ДМС с первого месяца. — Обучение и конференции за счёт компании. — Well-being программа. — Офисы в 10 городах России. Основные задачи проекта: — Развитие поиска и рекомендаций. — Улучшение продуктовых метрик через ML-модели. — Работа с высоконагруженным поисковым кластером. — Полный цикл ML: данные → модель → production → A/B-тест. О компании: — Крупный технический департамент. — Около 600 специалистов в IT, Data и Product. — 60+ команд. — ML-продукты используются миллионами пользователей.

Location: Paris, France Workplace: Hybrid HEALTH CAN’T WAIT. Not for symptoms to get worse. Not for a six‑month appointment. Not for a system to catch up. But that’s exactly how healthcare works today. You wait, until you can’t. Alan exists to end the wait. Health is a universal right, and we believe this right can only become real when it’s coupled with prevention. We need to stop treating health as something we repair and start treating it as something we build, every day. It’s not solely a question of willpower. It’s the healthcare system itself that needs to work for everyone, in a sustainable way. So we are building the new standard in prevention insurance. Alan is the first company that integrates insurance, prevention, and care into a single, acclaimed user experience. We are on an incredible journey to build a global leading company, with a unique culture https://alan.com/en/careers. We already partner with 40K+ companies of all sizes, serving more than 1M+ members, and have reached €800M+ in ARR. Prevention as the new norm. That's what we're building with our team of 1000+ people. If it speaks to you: we're hiring across France, Spain, Belgium, and Canada. And beyond. MISSION Alan is scaling internationally (800+ employees, 100+ healthcare professionals, 5+ countries) and needs IT Engineers to build and own the internal technology platform every Alaner works on: identity and provisioning, endpoint and SaaS security, the tooling behind our clinics, and the automation that keeps IT from scaling linearly with headcount. The scope below is what the team owns, not a checklist for one person. You'll be staffed on one or two of these areas at a time, according to your strengths and interests, and it will evolve through time based on company needs and personal appetite. Staying close to users keeps us building the right things: you'll spend up to 2-3 days a month on IT support, seeing first-hand where friction lives and what's worth automating next. SCOPE ⚙️ PLATFORM & AUTOMATION - Own and extend automations on Alan Home, our internal provisioning engine - Maintain, extend and improve helpdesk automation, SaaS provisioning flows, and onboarding/offboarding pipelines - Own the meeting room AV and room-booking platform as a product, replicable across offices 🔑 IDENTITY & ACCESS - Own the IT side of the employee lifecycle (onboarding, offboarding, mobility between jobs and countries) across Google Workspace, SSO/SAML and our SaaS ecosystem. - Grow our role-based access model: extend it to more tools and refine the permission groups, so access is granted by role rather than case by case - Make employee lifecycle events reliable, fast and auditable end-to-end - Own access reviews and least-privilege models; produce and maintain the evidence our auditors rely on - Own SaaS procurement and license management: vendor relationships, renewals, and cost optimization across our stack 🔒 ENDPOINT & SAAS SECURITY - Own the Mac fleet in Kandji: enrollment at scale, role-based blueprints, fleet compliance - Cloudflare WARP, SaaS security controls - Maintain and strengthen our ISO 27001 certification and drive ACPR and pentest remediation on the endpoint and platform scope 🩺 HEALTH PROFESSIONALS IT - The IT layer behind Alan's clinics: practitioner tooling, equipment, access - Role and permission automation for healthcare professionals, and their support and escalation process 🌍 INTERNATIONALISATION - Make international expansion something IT accelerates, not something it slows down: a repeatable process where every new market (Canada, KSA, Armenia...) launches on our standard stack from day one - Bring acquired entities onto that same platform MILESTONES First 3 months - Acquire technical knowledge on Alan's platform: Alan Home, Google Workspace/SSO, Kandji, Cloudflare WARP - Take one project end-to-end and ship a first increment - Join the IT oncall team as technical escalation First 6 months - Own a workstream end-to-end - Deliver one automation or hardening initiative end-to-end - Take up documentation and automation on your scope and reduce significantly the bus factor on it - Mentor IT teammates on your core expertise First year - Deliver measurable IT toil reduction on your scope - Set the technical standards on your domain: how we build, document and review it - Build strong cross-functional partnerships (Engineering, Security, Workplace, Clinic, People, Audit) - Contribute to hiring and onboarding new teammates 3 years - Lead major projects end-to-end across several communities - Be the reference the team relies on across at least two domains - Mentor the IT team and beyond, and scale IT without a linear increase in manual work As the community grows, there is room to take on more responsibility: leading a crew, owning cross-community projects, mentoring, hiring. WHAT WE'RE LOOKING FOR Must-have - Technical/hard skills: - 5+ years in IT engineering, platform, systems or corporate engineering, with real ownership of production-facing systems - Depth in at least one area (platform & automation, identity, endpoint & security) and demonstrated range beyond it. - You write code: not necessarily as a full-time software engineer, but Python, APIs and pull requests in a shared repo should feel normal - Structured problem-solving: diagnoses methodically, asks the right clarifying questions, thinks about failure modes and rollback - Comfort with regulated environments: health data, ACPR, ISO 27001, HDS - Automation & AI appetite: uses AI tools smartly to accelerate their own delivery, with an instinct for what should be automated or delegated to AI rather than repeated Must-have - Behavioral / soft skills: - Ownership & autonomy: drives topics forward without hand-holding, including when they cross into someone else's perimeter - Written communication: Alan is written-first; decisions get argued in public documents and challenged by people outside your team - Pragmatic judgment: balances "perfect" vs. "good enough", decides with incomplete information - Documentation culture: reduces bus factor through clear documentation and knowledge sharing Nice-to-have skills / Bonus points if you have: - Experience with an internal provisioning or IdP-driven automation platform - Kandji or another MDM at scale - Cloudflare Zero Trust / WARP, Datadog - Audit remediation experience (ISO 27001, HDS) - Multi-country rollouts, or IT integration after an acquisition - Having mentored, coached or led a (small) crew - Health or healthtech ecosystem familiarity Languages: Fluent English & Professional French

Location: Germany (Remote) At thinkcell, we’re seeking bright, creative C++ Developers with a solid theoretical background. Whether you're a seasoned professional or a recent graduate in Computer Science, Physics, or Mathematics, what matters most is your problem-solving ability and C++ skills. At thinkcell, we combine deep technical challenges with a healthy work-life balance and a culture that values thoughtful engineering over rushed delivery. Ideal candidates have: Strong C++ programming skills (through education or experience) A background in Computer Science, Mathematics, or Physics A passion for elegant, efficient algorithms Ability to work independently and creative problem-solving approaches Good command of English (German not required) How You’ll Work You’ll collaborate closely with the team, without being stuck in recurring meetings. Your feature ships when you think it’s ready - no time pressure! We encourage contributions from anyone What We Offer Brilliant Team: Work with international colleagues with strong academic and professional experience Work–life balance: Flexible working hours and an environment that respects your time and focus. Stable Environment: Join a market leader with a reliable product. Competitive salary & Relocation Support to Berlin: Work remotely from anywhere in Germany, while candidates based in Berlin are expected to work on-site. Guaranteed raise to 130k EUR after the first year Our Values As part of our team, you will embody and help shape the following principles that define us: Forward Thinking : We embrace change and challenge the status quo. Ownership : We take pride in our work and learn from our mistakes. Customer First : We prioritize delivering lasting value. Unified Team : We foster collaboration, respect, and integrity. Strive for Excellence : We set ambitious goals and pursue quality. Why is thinkcell the right place for you? We empower over 1.3 million users across 35,000 companies, including top consulting firms and major global enterprises. With offices in Berlin, Denver, Boston, London, Tokyo, and Dubai, we’re a diverse team of over 210 people, committed to innovation and excellence. thinkcell is more than a job – it's a chance to be part of an innovative, inclusive team that values collaboration and growth. We encourage applicants from all backgrounds to apply. Together, we can shape the future of productivity software. We can’t wait to see what you’ll bring to the team!

Location: Quebec Workplace: onsite (Version française ci-dessous) Game Engine QA  With multiple awards to our name, Larian Studios has proven that we’re dedicated to delivering high-quality role-playing games As we move on to new projects, the studio that brought you Baldur’s Gate 3 & Divinity: Original Sin 2 is looking for a Game Engine QA to join the team and help us in crafting the high calibre games our fans have come to expect. Responsibilities: Perform all kinds of manual testing Closely assist the programming team with testing specific changes before code is submitted to the main developer branches Write new and maintain existing automated tests (Python/Pytest and Lua) Follow priorities set by the QA Lead to ensure team-wide goals are met Test and help improve our production pipelines and tools Act as the first point of contact for the production team on technical issues Requirements:  Experience in programming and/or scripting Familiarity working with game engines Background using Perforce or other version control systems Good judgment in estimating the significance and consequences of bugs A rigorous, methodical, and resourceful approach to your work Self-disciplined, with strong self-learning skills Good communication skills, spoken and written, in English Passion for video games Desire to build a long-term career in QA Nice To Haves

Location: Amsterdam, Netherlands; Berlin, Germany; London, United Kingdom; Poland; Prague, Czech Republic; Tel Aviv, Israel; Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Tokyo, Japan 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 As a Partners Solutions Architect on the Applied AI team at Anthropic, you will be a Pre-Sales architect focused on cultivating technical relationships with our Global and Regional System Integrators (GSIs/RSIs), and our cloud partners (AWS and GCP). You will strengthen our relationships with key partners to accelerate indirect revenue, enable their AI practices, and execute on long-term GTM strategy. Key Responsibilities Strategic Technical Partnership : Be a technical thought partner to the Anthropic GTM partnerships team, providing technical expertise to better understand the partner landscape, driving key strategic programs, and identifying opportunities to deepen partner technical capabilities. Embed with GSI and cloud partner technical teams to enable their AI practices, support troubleshooting, evangelize Anthropic in their developer communities, and serve as an escalation point for complex technical issues. Joint Solution Development: Collaborate with partners to identify high value industry-specific GenAI applications, develop joint solutions and codify reference architectures / best practices to accelerate time to deployment Customer Deal Support: Intervene directly to unblock strategic customer deals where partners are the primary delivery vehicle, providing deep technical expertise and solution architecture guidance. Partner Ecosystem & Events : Represent Anthropic at partner events such as GSI customer workshops, AWS summits, and industry conferences. Lead or support partner-specific developer events, hackathons, and technical enablement sessions, especially for technically native communities. Product Feedback: Validate and gather feedback on Anthropic's products and offerings, especially as they relate to partner use cases and deployment patterns, and deliver this feedback to relevant Anthropic teams to inform product roadmap and partner strategy. Preferred qualifications 5+ years of experience in technical customer-facing/partner-facing roles such as Solutions Architect, Sales Engineer, Partner Sales Engineer, Technical Account Manager Track record of successfully partnering with GSIs and/or cloud providers to solve complex technical challenges, from initial solution design through customer delivery Exceptional ability to build relationships with and communicate technical concepts to diverse stakeholders to include C-suite executives, engineering & IT teams, and more Strong presentation & technical communication skills with the ability to translate requirements between technical and business stakeholders Experience designing scalable cloud architectures and integrating with enterprise systems Familiarity with common LLM frameworks and tools or a background in machine learning or data science Excitement for engaging in cross-organizational collaboration, working through trade-offs, and balancing competing priorities A love of teaching, mentoring, and helping others succeed Passion for thinking creatively about how to use technology in a way that is safe and beneficial, and ultimately furthers the goal of advancing safe AI systems Fluent in Japanese and English 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: London Workplace: OnSite Company Description We’re http://checkout.comCheckout.com http://Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact. With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started. The Role Checkout is looking for an experienced financial crime compliance lead to establish, grow, and maintain a global second-line team. This role will oversee the compliance frameworks supporting our rapidly evolving portfolio of mass market products. This opportunity is a perfect fit if you are excited by the prospect of leveraging the momentum and resources of a global scale-up to support the launch and scale of high velocity products. As products expand and launch, there will be an opportunity to grow your team across multiple regions. You will be expected to: - Build the Framework: Establish the global framework for second-line escalations for new products; working closely with relevant regional MLROs to ensure consistency and operational excellence. - Drive Cross-Functional Alignment: Partner with Risk, Operations, and Product teams to align compliance requirements with fraud mitigation and user experience goals. - Monitor Emerging Trends: Maintain a deep understanding of financial crime typologies, regulatory guidance, and emerging industry trends, ensuring these are proactively factored into our control framework and knowledge base. - Design & Deliver Training: Develop and deliver targeted training to business and compliance teams regarding complex financial crime and fraud risks inherent in high-volume environments. - Ensure Compliance-by-Design: Partner with our Regulatory Compliance function to integrate compliance requirements early in the product development lifecycle. - Own Compliance Requirements: Assume central responsibility for mapping and maintaining SDD, CDD, and EDD requirements for our evolving product set, ensuring relevant factors are codified into global minimum standards. - Strategic Advisory: Directly advise Product and Operations teams on the opportunities and risks associated with automation, AI deployment, and system scale. - Integrate Risk Assessments: Collaborate closely with regional MLROs to ensure product-specific risk profiles are accurately captured and factored into Business-Wide Risk Assessments. - Implement Assurance: Collaborate with our Assurance and Internal Controls team to design and execute robust compliance monitoring processes for new product lines. Experience we are looking for: - 6+ years of second-line compliance experience in fintech, payments, or banking at scale (neobank/fintech experience strongly preferred). - Deep expertise in financial crime (AML/CFT/Sanctions/Fraud) risk, with regulatory compliance experience as a strong plus. - Experience growing and leading a team, with cross-border/global management experience highly desirable. - Proven track record in architecting frameworks for high-volume environments and working directly with technical/product stakeholders to design solutions. Personal qualities we look for: - Confident decision-maker: Ability to quickly assess risks, weigh mitigations, and provide strong, defensible rationale for your decisions. - Technical fluency: Comfortable working directly with technical leaders to drill down into the configuration of user journeys, systems, and tooling. - AI-first mindset: Experience deploying AI or automation tools to accelerate compliance processes at scale. - Strategic thinker: Ability to immerse yourself in fine details while keeping the broader organisational picture in mind. - Growth-oriented: Relentlessly curious and driven; always learning and spotting opportunities to develop yourself and others. - Proportionate and Collaborative: An obsession with balancing strong compliance with scalability and user experience, supported by exceptional interpersonal and communication skills. Additional Information Bring all of you to work We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands. We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here. It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable. Life at Checkout.com http://Checkout.com We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection. Curious about what it’s like to be part of our team? Visit our Careers Page https://www.checkout.com/careers to learn more about our culture, open roles, and what drives us. For a closer look at daily life at Checkout.com http://Checkout.com, follow us on LinkedIn https://www.linkedin.com/company/checkout/life/ and Instagram https://www.instagram.com/checkout_com/