SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Higgsfield AI is hiring a Backend Software Engineer to own the trust and safety infrastructure that protects accounts, credits, payments, and generation from fraud and abuse.
You will take ML-generated fraud signals and risk scores from the analytics team and build the backend systems that enforce protections reliably in production. Your responsibilities include:
- Risk Integrations: Connect ML scores and risk signals into account creation, credit usage, payments, and generation flows with proper timeouts and fallback behavior.
- Enforcement Services: Implement configurable rules engines, rate limiters, account restrictions, and credit/payment controls with consistent behavior across services.
- Operator Tooling: Build APIs and workflows for case review, decision history inspection, and safe application or reversal of restrictions.
- Production Reliability: Handle retries, duplicate events, concurrent requests, and safe rollouts. Add monitoring and audit trails so failures can be traced and fixed.
- Cross-team Collaboration: Work in other teams' codebases to add signals or interventions without compromising stability or architecture.
- Ownership: Clarify control requirements with ML, analytics, and operations teams; scope the smallest useful implementation; ship quickly (release cycle is days, not quarters); verify production behavior and review impact with the team.
The role is ideal for an engineer who can take an unclear problem to a working production system, who thrives on ownership and rapid iteration, and who can communicate decisions and results clearly. You'll work closely with ML, analytics, and operations to translate risk requirements into reliable backend behavior, using operator feedback and customer cases to continuously improve controls.
REQUIREMENTS:
- Demonstrated ability to take a difficult problem from an unclear starting point to a working production system; ability to explain your own decisions, contribution, and results.
- Strong backend engineering fundamentals: APIs, data models, queues, transactions, idempotency, and concurrency. Experience integrating external services and debugging failures across request and event lifecycles.
- Self-directed problem-solving: you choose next steps, remove blockers, ask for help when needed, cut scope to test ideas, adapt from evidence, and persist through difficult problems.
- Ability to work with ML, analytics, and operations teams to translate risk requirements into clear backend behavior. Quick learner for unfamiliar systems.
- Collaborative mindset: you take feedback, change your mind when evidence changes, share knowledge and responsibility, and do the work the team needs.
NICE TO HAVE:
- Fraud prevention, payments risk, or platform abuse experience.
- Experience integrating risk-scoring services, building rules engines, or implementing rate limiters.
- Case management, appeals, and risk operations tooling experience.
- Familiarity with Stripe.
- Experience with real-time scoring or graph-based account linkage systems.
- Python or Go experience.
