Location: Malaysia; Kuwait; Oman; Amman, Jordan; Saudi Arabia
Workplace: remote
We’re running a short video data collection project for one of our clients. We’re looking for native Arabic speakers across multiple Arabic-speaking countries and dialects to record a 15–20 second video, speaking naturally in Arabic and looking directly into the camera. The videos collected will be used to help train and evaluate an AI model that distinguishes real, human-recorded video from AI-generated (synthetic) video.
Project Details:
Start Date: ASAP
Duration: 1 session
Commitment: 1 hour.
Job Type: Freelance
Location: Malaysia/Oman/Kuwait/Jordan/Türkiye/UAE/Saudi Arabia/Qatar (Remote)
Language/Dialects:Yemeni Arabic
Pay Rate: $4.00-$15.00 (Depending on country location)
Eligibility
18 years of age or older.
Native Arabic speaker (self-identified). You may be asked to complete a brief screening step to confirm your dialect before your submission is finalized.
What you’ll do
Record a single 15–20 second video in your native Arabic
dialect
.
Speak naturally (no script). Suggested prompts: briefly introduce yourself, mention where you live, or talk about an interest/hobby.
Look directly at the camera the whole time.
After recording, answer a few short questions about yourself (e.g. age range, country, dialect) to help us organize the dataset.
Recording specifications (must meet all to be accepted)
Video quality: 1080p or higher
Length: 15–20 seconds
Framing: Head-and-shoulders, face centered and fully visible
Lighting/background: Well-lit face (no harsh shadows), plain/neutral background
Expression: Neutral/relaxed while speaking naturally
Audio: Clear speech, no background noise, music, or echo
Orientation: Portrait or landscape acceptable (hold steady, no shaky footage)
Environment: Quiet indoor setting preferred
Prohibited: Filters/beauty modes, sunglasses, hats/hoods, face coverings, headphones/earbuds, watermarks, stickers, borders, other people/pets in frame
Your Privacy
Before you begin, you’ll be asked to review and accept a consent form explaining how your video and any information you provide will be used, stored, and who it will be shared with.
УдалённоMalaysia; Kuwait; Oman; Amman, Jordan; Saudi Arabia
Location: Amman, Jordan; Kuwait; Oman; Dubai, UAE
Workplace: remote
We’re running a short video data collection project for one of our clients. We’re looking for native Arabic speakers across multiple Arabic-speaking countries and dialects to record a 15–20 second video, speaking naturally in Arabic and looking directly into the camera. The videos collected will be used to help train and evaluate an AI model that distinguishes real, human-recorded video from AI-generated (synthetic) video.
Project Details:
Start Date: ASAP
Duration: 1 session
Commitment: 1 hour.
Job Type: Freelance
Location: Malaysia/Oman/Kuwait/Jordan/Türkiye/UAE/Saudi Arabia/Qatar (Remote)
Language/Dialects:Levantine Arabic
Pay Rate: $4.00-$15.00 (Depending on country location)
Eligibility
18 years of age or older.
Native Arabic speaker (self-identified). You may be asked to complete a brief screening step to confirm your dialect before your submission is finalized.
What you’ll do
Record a single 15–20 second video in your native Arabic
dialect
.
Speak naturally (no script). Suggested prompts: briefly introduce yourself, mention where you live, or talk about an interest/hobby.
Look directly at the camera the whole time.
After recording, answer a few short questions about yourself (e.g. age range, country, dialect) to help us organize the dataset.
Recording specifications (must meet all to be accepted)
Video quality: 1080p or higher
Length: 15–20 seconds
Framing: Head-and-shoulders, face centered and fully visible
Lighting/background: Well-lit face (no harsh shadows), plain/neutral background
Expression: Neutral/relaxed while speaking naturally
Audio: Clear speech, no background noise, music, or echo
Orientation: Portrait or landscape acceptable (hold steady, no shaky footage)
Environment: Quiet indoor setting preferred
Prohibited: Filters/beauty modes, sunglasses, hats/hoods, face coverings, headphones/earbuds, watermarks, stickers, borders, other people/pets in frame
Your Privacy
Before you begin, you’ll be asked to review and accept a consent form explaining how your video and any information you provide will be used, stored, and who it will be shared with.
Location: Saudi Arabia; Oman; Kuwait; Dubai, UAE; Amman, Jordan
Workplace: remote
We’re running a short video data collection project for one of our clients. We’re looking for native Arabic speakers across multiple Arabic-speaking countries and dialects to record a 15–20 second video, speaking naturally in Arabic and looking directly into the camera. The videos collected will be used to help train and evaluate an AI model that distinguishes real, human-recorded video from AI-generated (synthetic) video.
Project Details:
Start Date: ASAP
Duration: 1 session
Commitment: 1 hour.
Job Type: Freelance
Primary Location: Malaysia/Oman/Kuwait/Jordan/Türkiye/UAE/Saudi Arabia/Qatar (Remote)
Language/Dialects: Gulf Arabic
Pay Rate: $4.00-$15.00 (Depending on country location)
Eligibility
18 years of age or older.
Native Arabic speaker (self-identified). You may be asked to complete a brief screening step to confirm your dialect before your submission is finalized.
What you’ll do
Record a single 15–20 second video in your native Arabic
dialect
.
Speak naturally (no script). Suggested prompts: briefly introduce yourself, mention where you live, or talk about an interest/hobby.
Look directly at the camera the whole time.
After recording, answer a few short questions about yourself (e.g. age range, country, dialect) to help us organize the dataset.
Recording specifications (must meet all to be accepted)
Video quality: 1080p or higher
Length: 15–20 seconds
Framing: Head-and-shoulders, face centered and fully visible
Lighting/background: Well-lit face (no harsh shadows), plain/neutral background
Expression: Neutral/relaxed while speaking naturally
Audio: Clear speech, no background noise, music, or echo
Orientation: Portrait or landscape acceptable (hold steady, no shaky footage)
Environment: Quiet indoor setting preferred
Prohibited: Filters/beauty modes, sunglasses, hats/hoods, face coverings, headphones/earbuds, watermarks, stickers, borders, other people/pets in frame
Your Privacy
Before you begin, you’ll be asked to review and accept a consent form explaining how your video and any information you provide will be used, stored, and who it will be shared with.
УдалённоSaudi Arabia; Oman; Kuwait; Dubai, UAE; Amman, Jordan
Задачи:
• Владение архитектурой и поставкой сервисов прогнозирования, ранжирования и рекомендаций.
• Проектирование воспроизводимых рабочих процессов обучения, оценки, мониторинга и переобучения.
• Перевод целей продукта в измеримые задачи машинного обучения, эксперименты и релизы.
Требования:
• Более 5 лет профессионального опыта в разработке программного обеспечения или машинного обучения с производственными системами.
• Продвинутые знания Python и SQL; сильные навыки в статистике, экспериментах и оценке моделей.
• Опыт работы с оркестрацией, контейнерами, облачной инфраструктурой, наблюдаемостью и CI/CD.
• Умение отлаживать ошибки данных и моделей.
• Опыт в прогнозировании, ранжировании, рекомендациях, оптимизации или другой прикладной области машинного обучения.
• Владение английским языком для технической документации и межфункционального взаимодействия.
Задачи:
— Полное владение датасетами безопасности: сбор, очистка, разметка, контроль качества, версионирование и подготовка к обучению и оценке моделей.
— Перевод требований политики безопасности в четкие и последовательные критерии разметки и оценки, в тесном сотрудничестве со специалистами по политике.
— Проектирование и управление процессами разметки, включая поиск, адаптацию и контроль внешних участников для обеспечения высокого качества.
— Создание рабочих процессов оценки моделей в продакшене с использованием реальных данных для отслеживания производительности и выявления проблем.
— Разработка легковесных пайплайнов и инструментов на Python и SQL для ускорения и воспроизводимости работы с данными, в партнерстве с ML-инженерами по требованиям к "готовности к обучению".
Требования:
— Опыт работы Data Scientist или аналогичной роли с реальными датасетами и ML-моделями.
— Глубокое понимание качественного датасета: сбор, очистка, выборка, разметка, контроль качества, оценка.
— Уверенное владение Python и SQL для анализа и практических рабочих процессов с данными.
— Сильное критическое мышление и способность принимать решения в условиях неоднозначности, умение превращать сложные руководства в последовательные и масштабируемые решения.
— Автономность и хорошие коммуникативные навыки, способность работать в междисциплинарной команде (политика, инженерия, исследования) в быстро меняющейся среде.
Преимущества:
— Опыт в AI safety, trust & safety, модерации контента или обнаружении мошенничества/злоупотреблений.
— Опыт создания датасетов или фреймворков оценки, опыт с adversarial/safety-ориентированным ML.
— Опыт управления процессами разметки с участием человека, версионирования датасетов или инструментов ML-пайплайнов.
— Опыт внедрения руководств совместно с командами политики или юридическими отделами.
Условия:
— Работа в гибридном формате в Лондоне, тесное взаимодействие с командами AI Safety и Policy.
— Равные возможности для всех кандидатов без дискриминации по различным признакам.
Ipotekabank — банк в Узбекистане, входящий в международную финансовую OTP Group, специализирующийся на ипотечном и розничном кредитовании.
Вакансии:
- Senior Software Engineer (AI)
- Senior/Middle Data Engineer
Офис в Ташкенте.
Ipotekabank — банк в Узбекистане, входящий в международную финансовую OTP Group, специализирующийся на ипотечном и розничном кредитовании.
Вакансии:
- Principal QA Engineer (AI Engineering)
- Senior QA Engineer
- Senior QA специалист
Офис в Ташкенте.
Требуется преподаватель русского языка для обучения нейросетей. Вакансия предполагает полный рабочий день с возможностью удалённой работы. Требуемые навыки включают копирайтинг, управление контентом, редактуру и корректуру, журналистику, контент-маркетинг. Грейд — middle.
Задачи:
- Разработка и развитие микросервисов и data-процессов для online/streaming и batch обработки
- Построение пайплайнов обработки, обогащения и репликации данных
- Создание витрин, агрегатов и ML-фичей для антифрод-моделей
- Работа с большими объёмами исторических данных и тяжёлыми расчётами
- Оптимизация производительности и надёжности сервисов и пайплайнов
Требования:
- Коммерческий опыт разработки на Python
- Опыт создания backend-сервисов и data-пайплайнов, понимание микросервисной архитектуры
- Опыт работы с большими объёмами данных, понимание batch и streaming обработки
- Опыт работы с реляционными и in-memory базами данных
- Понимание принципов работы ML-пайплайнов
- Будет плюсом: опыт в Anti-Fraud / Risk / Scoring, low-latency системах; Spark, Kafka, Tarantool или Ignite
Условия:
- Формат работы: удалённо
- Пятница — сокращённый рабочий день
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, 25M+ 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
This role owns how Higgsfield measures its product - from the first sign-up to whether people come back.
You are the measurement owner for onboarding, first generation, the paywall, plans and credits, and retention. Product managers ship fast here - your job is to make sure they ship knowing.
You make sure:
- Every product decision has a number attached to it before it ships and after it ships
- Experiments are designed to be readable, and then read honestly - including when the answer is “this didn’t work”
- The events the product emits can be trusted; you own the definition, not just the query
- A PM gets an answer in hours, not next sprint
You are the person who decides what “it worked” means.
WHAT YOU WILL DO
Product measurement
- Own the core product metrics: activation, first-generation success, generation depth, free→paid conversion, repeat usage, retention by cohort and segment.
- Define each metric once, write the definition down, and hold the line on it - one definition across dashboards, decks, and Slack.
- Instrument new features before launch, with product and engineering: which events, which properties, what success looks like, when we call it.
- Watch the health of the event stream itself - tracking drift, double counting, missing parameters, naming breakage and find the problem before a decision gets made on top of it.
Experimentation
- Design A/B tests properly: hypothesis, one primary metric, unit of randomization, MDE, sample size, run length, guardrail metrics.
- Read them honestly: significance, peeking, novelty effects, sample-ratio mismatch, segment heterogeneity.
- Separate “we proved this works,” “we proved this doesn’t,” and “we can’t tell from this test” - and say which one out loud.
Product & monetization analysis
- In-product funnel work: onboarding and quiz, first generation, paywall, checkout, plan choice, credit top-ups.
- Pricing and packaging analysis: plan mix, credit consumption, unit economics per generation, margin by feature and by model.
- Feature adoption and its real effect on retention and revenue — separating “users who do X retain better” from “making people do X improves retention.”
- Behavioural segmentation: casual creators vs corporate/B2B, new vs returning, by model and by use case. Mixing them hides everything that matters.
Making it usable
- Build the few dashboards PMs actually open on their own — decision-shaped, not comprehensive.
- Write short readouts a non-analyst can act on. Visualize data for humans, not for other analysts.
- Automate recurring reporting so your hours go to new questions, not refreshes.
- Work in SQL, Python, BigQuery and product analytics tools. We use AI heavily - resourcefulness beats syntax.
WHO WE'RE LOOKING FOR
We're looking for an analyst with opinions.
You should have:
- Strong SQL: window functions, cohorts, funnels and retention on raw event data, without help.
- Python at working level for analysis (pandas, notebooks). Entry level is fine - we use AI heavily.
- Real experimentation experience: you have designed tests, run them, and killed features with the results.
- Statistical honesty: you know what a p-value does and does not entitle you to say, and you have refused to call a flat test a win.
- Understanding of subscription + one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, deferred value of unspent credits.
- The instinct to check the instrument before explaining the movement - a surprising number is a claim, not a fact.
- Ownership: you decide what's worth analyzing, you chase the fix, you follow the recommendation to a shipped change.
- Clear written and spoken English, B2+.
Backgrounds that often do well:
- Product analysts from consumer subscription or PLG products
- Growth/BI analysts who moved into product and stayed
- Early-startup analysts who built product measurement from zero
- Data scientists who got tired of models and want decisions
WHAT THIS ROLE IS NOT
This role is not a fit if you:
- Wait for a ticket to tell you what to analyze
- Want to build models more than you want to change decisions
- Need a data engineering team and clean tables to exist before you can start
- Would report a lift you don't believe in because a stakeholder wants it
- Need perfect data before you can say anything useful
- 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)
WHAT WE OFFER
- Competitive base salary in USD, based on your experience, skills, and the scope of the role.
- Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth.
- Relocation support to Almaty for candidates moving from another city or country.
- A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company.
- Opportunities for professional growth, ownership, and career development as the company scales.
- Company-provided equipment, meals, transportation, or other office benefits.
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, 25M+ 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
Right now we treat 25M+ people as one user. The same paywall, the same discount, the same lifecycle email, the same retention effort. And we mostly conclude that a campaign worked by looking at what the people we sent it to did next — which is not the same thing.
This role makes the company act per user, and know what that action was worth.
You own both halves of that: the models that decide who we aim at, and the causal reads that say whether it landed. Who is about to leave. Who will come back on their own without us spending anything. Whose decision a discount actually changes. And what a payer is worth over their life, once the discount, the refunds and the compute they consume are all inside the number.
You make sure:
- Discounts and credit vouchers go to the people whose behaviour they change, and nowhere else
- We know who is about to churn early enough to do something about it — and we know whether the something worked
- Lifetime value is a number we can defend by cohort, by plan and by the discount a cohort was acquired on, not one blended average that hides all three
- Every model and every campaign has a holdout behind it, so we can always state what it is worth in money
WHAT YOU WILL DO
Churn, retention & lifecycle propensity
- Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for one-time credit-pack buyers — two different machines, not one model with a flag on it.
- Know the difference between a model that predicts churn and a model that reduces it. AUC earned by detecting users who already stopped using the product is worth nothing.
- Hunt leakage relentlessly. Cancellation-adjacent features will hand you a beautiful offline number and a useless system.
- Pair every score with an intervention and a holdout. The deliverable is a retained user, not a ranked list.
Uplift modelling & offer targeting
- Own incrementality on everything we aim at a user: discounts, credit vouchers, trials, upgrade prompts, win-back campaigns, retention saves.
- Model uplift, not propensity.Targeting the users most likely to convert spends margin on people who were going to convert anyway; the entire value of this work is finding the users whose decision the offer changes.
- Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently — including the ones marketing will ask you to switch off.
- Respect the guardrails. An offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find the abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup.
- Work with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets — you should want that conversation, not route around it.
Lifetime value & payer economics
- Own LTV: cohort-based, censored honestly, stated on contribution margin rather than gross revenue, and split by plan, segment and acquisition discount.
- Model the parts that actually move it — refunds and chargebacks, credit breakage and expiry, plan migration and downgrades, and the compute an unlimited plan consumes.
- Make it a decision input, not a slide: what we can pay for a user, which discount depth pays back, which plan we should stop selling.
- Keep it current and versioned. A stale LTV curve is worse than no curve, because people act on it.
Experimentation & causal measurement
- Design and read the experiments behind pricing, packaging, offers and lifecycle — with the product analytics team, not in parallel to it.
- Reach for the right tool when a clean experiment is impossible: difference-in-differences, matching, switchback and geo tests, synthetic control — and name the assumption each one is buying.
- Separate "users who do X retain better" from "making users do X improves retention." Every time, out loud.
- Kill your own results. A finding that doesn't survive a second look should die at your hands, not in a board deck.
How we ship
- Own the work end to end: framing the decision, features, training, evaluation, the scored audience landing in the tool that sends the offer, monitoring, retraining.
- Models ship as batch scores and policy rules into lifecycle tooling, the paywall and pricing — plus the readout that says what they earned. There is no real-time serving stack to babysit.
- Monitor for drift. New model launches, price changes, discount campaigns and seasonality all move the ground under a deployed score.
- Write down what each model is worth, in money, and keep that number current.
WHO WE'RE LOOKING FOR
- Experience shipping models that changed what the business did to real users— a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this.
- Depth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale.
- Genuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift.
- Strong SQL and Python: cohorts, funnels and retention on raw event data without help; gradient boosting; and the boring parts — feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot.
- Understanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer.
- Commercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model.
- Pragmatism. A segment rule shipped this month that lifts retention beats a two-quarter uplift platform. Then you replace the rule.
- Clear written and spoken English, B2+.
Backgrounds that often do well:
- Growth, retention or monetization data scientists from subscription, gaming, fintech or e-commerce — anywhere offers and churn are modelled with real money attached
- CRM and lifecycle data scientists who owned campaign targetingandits incrementality
- Causal inference and uplift specialists who ship policies rather than papers
- Strong product analysts who moved into modelling and kept the decision instinct
WHAT THIS ROLE IS NOT
This role is not a fit if you:
- Want to train or fine-tune generative video models — that's what our R&D ML Engineer roles do, and they're open
- Want to own a recommender or a serving stack — ranking is not in this role's scope today
- Would target an offer at the users most likely to buy and call it personalization
- Report a lift from a before-and-after comparison, or from comparing the users you targeted to everyone you didn't
- Optimize AUC and hand the score to someone else to work out what to do with it
- Need clean labelled data, a feature store and an experimentation platform to exist before you can start
- Want predictable 9–5 workdays
HIRING PROCESS
We move fast:
- Screening call (30 min)
- Technical interview & case study (1 hour)
- Practical home task (7 days)
- Team interview (60 min)
- Paid on-site trial (1 month)
WHAT WE OFFER
- Competitive base salary in USD, based on your experience, skills, and the scope of the role.
- Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth.
- Relocation support to Almaty for candidates moving from another city or country.
- A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company.
- Opportunities for professional growth, ownership, and career development as the company scales.
- Company-provided equipment, meals, transportation, or other office benefits.
