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Алматы, Қазақстан
Onsite
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.
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
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.
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
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
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)
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