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Backend EngineerUnited States Digital Space LLC • Palo Alto, California, United States
Postuler Maintenant
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Backend Engineer

United States Digital Space LLC
  • US
    Palo Alto, California, United States
  • US
    Palo Alto, California, United States
Postuler Maintenant

À propos

About the company: The company is building the AI-first trust and operations layer for global commerce. We partner with leading platforms, marketplaces, payment providers, and banks to transform how business onboarding, monitoring, and lifecycle decisions are made—using AI on the ground to drive faster, smarter actions with less friction.
Our customers describe us as a combination of Cut‑the‑cord and Lovable for risk teams: flagging bad actors, assisting in investigations, and autonomously acting to mitigate fraud losses in real time.
Backed by top-tier investors such as Y Combinator, Lux Capital, Pathlight Ventures and founded by experts in the payments domain, the company is reimagining how risk and ops gets done—not with legacy rule engines, but with domain‑specific AI that thinks like your best risk analyst at scale. We help customers scale their expertise, move faster, and unlock growth without compromising safety.
We're growing quickly, with strong customer pull and adoption globally. This role is for an engineer who wants to avoid busywork that isn’t shipping, learns at the frontier of AI and risk infrastructure fast, and works on distributed systems problems around fraud detection.
Why this role matters Fraud detection and risk mitigation are fundamentally hard distributed systems problems: adaptive adversaries, data sparsity, and latency/scale constraints drive the need for engineers who can build resilient, real‑time decision engines.
What you’ll do Backend + AI (~80%)
Build and own Python/Django services that sit at the heart of risk decisioning and execution.
Model complex fraud/risk data in Postgres; write queries and aggregations that scale to billions of records.
Build, operate, and enhance data ingestion pipelines from Stripe, Adyen, and other payment processors—as well as systems of record like Salesforce and Zendesk—to handle near‑real‑time volume.
Integrate with poorly documented, inconsistent, and sometimes ancient APIs from legacy processors and platforms, turning that mess into reliable signal.
Build feature‑engineering pipelines that extract signals, including non‑obvious latent ones.
Frontend (~20%)
Implement customer‑facing features such as AI chat interfaces and graph visualizations.
Analyze, diagnose, and fix UI latency and performance bottlenecks.
Qualifications
1+ years building production systems in Python/Django with Postgres.
Experience building frontend applications with React.js and Next.js.
Use AI tools by default—LLMs, modern AI tooling—to move faster and consider how AI reshapes your systems.
Willingness to do the unglamorous integration work that others avoid, with grit to make undocumented, legacy systems behave.
Very high energy and high agency—self‑motivated, comfortable with long hours, and driven to go the extra mile.
Experimental but practical mindset: ship fast, measure rigorously, iterate.
Strong sense of information architecture and data presentation.
Nice to have
Internship or early‑career experience at a fast‑scaling fintech or infra company (e.g., Stripe, Plaid, Brex, Adyen).
Familiarity with payment infrastructure, merchant underwriting, or risk tooling.
Hands‑on experience fine‑tuning or optimizing LLMs or SLMs, ideally in fraud, anomaly detection, or adversarial domains.
Experience working closely with product or design in an early‑stage team.
How we work
Bias toward action, measurable impact, and staying ahead of adversaries.
Everyone owns their code in production—from training to inference to APIs.
Fast iterations with real customer feedback; clear metrics drive decisions.
In‑person culture—5 days a week in our Palo Alto office.
We go well beyond the usual 40/50 hours per week, requiring high‑energy, high‑agency people who go the extra mile.
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  • Palo Alto, California, United States

Compétences linguistiques

  • English
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