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About the role
Our ML and analytics teams already work on fraud detection and risk analysis. This role owns the backend that turns their signals into reliable protections across accounts, credits, payments, and generation. You'll integrate risk checks into product flows, build enforcement services and operator tools, and make sure controls work correctly in production. We're looking for an engineer we can trust to take a backend problem from an unclear starting point to a working solution. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- You've taken a difficult problem from an unclear starting point to a working system used in production, and can explain your own decisions, contribution, and results
- You can build reliable backend services: APIs, data models, queues, transactions, idempotency, and concurrency. You can integrate external services and debug failures across the request and event lifecycle
- You choose the next steps, remove blockers, and ask for help when needed. You cut scope to test an idea, adapt from evidence, and keep working through difficult problems
- You work with ML, analytics, and operations to turn risk requirements into clear backend behavior. You use operator feedback and customer cases to improve controls and learn unfamiliar systems quickly
- You take feedback, change your mind when the evidence changes, and do the work the team needs. You share knowledge and responsibility so others can operate and extend what you build
More about this role
Almaty, Kazakhstan · Trust & Safety Engineering
Our ML and analytics teams already work on fraud detection and risk analysis. This role owns the backend that turns their signals into reliable protections across accounts, credits, payments, and generation. You'll integrate risk checks into product flows, build enforcement services and operator tools, and make sure controls work correctly in production. We're looking for an engineer we can trust to take a backend problem from an unclear starting point to a working solution.
Risk integrations: connect existing ML scores and risk signals to account creation, credit usage, payments, and generation flows, with clear timeouts and fallback behavior.
Enforcement services: implement configurable rules, rate limits, account restrictions, and credit or payment controls with consistent behavior across services.
Operator tooling: build APIs and workflows for reviewing cases, inspecting decision history, and safely applying or reversing restrictions.
Production reliability: handle retries, duplicate events, concurrent requests, and safe rollouts; add monitoring and audit trails so failures can be traced and fixed.
Work in other teams’ codebases...
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