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Software Engineer, Trust & Safety

in 40210, Düsseldorf, Nordrhein-Westfalen, Deutschland
Unternehmen: Jobgether SRL
Vollzeit position
Verfasst am 2026-10-11
Berufliche Spezialisierung:
  • Software Entwicklung
    Backend Entwicklung, DevOps Ingenieur, AWS, Software-Ingenieur
Gehalts-/Lohnspanne oder Branchenbenchmark: 134000 - 156000 EUR pro Jahr EUR 134000.00 156000.00 YEAR
Stellenbeschreibung

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer, Trust & Safety based in Germany.

Join a fully remote engineering team building reliable email infrastructure for millions of developers worldwide. As a Software Engineer specializing in Trust & Safety, you will design and develop sophisticated anti-abuse systems that protect legitimate senders while preventing spam, phishing, fraud, and account compromise. You will build real‑time enforcement mechanisms, scalable detection pipelines, and automated workflows that turn security signals into effective action within milliseconds.

Working across backend services, infrastructure, databases, and machine learning, you will help strengthen platform security without compromising the experience of legitimate users. You will have significant ownership over technical decisions, from initial discovery through production deployment. This role offers the opportunity to solve complex engineering challenges in a quality-focused, collaborative, and globally distributed environment.

Accountabilities:
  • Build and operate real-time enforcement systems that evaluate and act on potential abuse within strict millisecond-level latency requirements.
  • Design scalable detection pipelines that transform signals from email sending, receiving, and account activity into decisions and automated actions.
  • Develop, maintain, and improve detection rules and enforcement policies, ensuring they can be updated safely and evaluated consistently.
  • Detect and contain account takeovers and leaked API keys by identifying suspicious behavioral changes and limiting potential damage while avoiding unnecessary account lockouts.
  • Establish feedback mechanisms that record the reasons behind enforcement decisions and reversals, measure detection precision, and identify rules that negatively affect legitimate senders.
  • Automate manual workflows, including investigations, appeals, and bulk enforcement actions, to improve operational efficiency as platform usage grows.
  • Build, evaluate, and deploy machine learning classifiers for spam and phishing detection, validating their precision before enabling automated enforcement.
  • Implement safeguards that reduce false positives and ensure enforcement decisions are reliable, explainable, and reversible where appropriate.
  • Maintain comprehensive observability through structured logs, metrics, tracing, error handling, and audit trails for all enforcement activities.
  • Contribute across the full technology stack, including frontend interfaces, backend services, databases, queues, and supporting infrastructure.
  • Collaborate with engineering colleagues and developers using the platform to understand real-world needs and deliver effective, dependable solutions.
  • Take ownership of projects from discovery and technical design through implementation, production deployment, and continuous improvement.
Requirements:
  • At least five years of professional software engineering experience.
  • Strong written and spoken English communication skills.
  • Deep hands‑on experience with Node.js, Type Script, and React, ideally including frameworks such as Hono and Next.js.
  • Experience with Go or a strong willingness to use it for latency‑sensitive backend services.
  • Solid infrastructure and reliability engineering skills, including experience with AWS, AWS CDK, and Datadog.
  • Strong understanding of APIs, event-driven architectures, queues, and stateful services.
  • Experience designing resilient systems that fail safely, with an emphasis on treating false positives as defects that must be investigated and addressed.
  • Strong observability practices, including metrics, logging, distributed tracing, error handling, and…
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