Staff Machine Learning Engineer

MoonPay · London - Hybrid

En corto

▸Ingeniero de ML de alto impacto que construye y mantiene sistemas en tiempo real para prevenir fraudes en transacciones.
▸Diario: desarrollas modelos, infraestructura de características y pipelines de decisión en producción con alta latencia y precisión.
▸Lo destacado: la IA es el modo operativo predeterminado; todo el trabajo se hace con herramientas de IA integradas.

Fluency in English is required for team collaboration and technical communication.

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¿Qué piden?

✓Experiencia en sistemas de servicio en tiempo real con alta disponibilidad.
✓Habilidades para construir y operar infraestructura de características en batch, near-real-time y en solicitud.
✓Capacidad para alinear entrenamiento y producción (training-serving consistency).
✓Experiencia en implementar feedback loops con decisiones reales y resultados contables.
✓Habilidad para liderar soluciones en ambigüedad y definir problemas complejos.
✓Conocimiento en enfoques de rollouts seguros: shadow, staged, replay.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonGogRPCKubernetesPostgreSQLRedisApache KafkaAirflowMLflowPrometheus

¿A quién escribirle en MoonPay?

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About MoonPay MoonPay is for builders with something to prove. This isn't a "work on cool crypto stuff" company. It's a high-standards, high-velocity, high-accountability company building the operating system for value movement. If the internet moves information, we move value: crypto, stablecoins, tokenized assets, and whatever comes next. Four offerings make that real: fund, tokenize, trade, and spend. 30M+ customers and 500+ ecosystem partners run on us. S. Regulated across the UK, EU, Canada, and Australia. AI is the default operating mode here. It's woven into every role, and we expect you to use it daily. It handles the manual work so you can deliver on what actually matters. You'll thrive here if outcomes excite you more than process, if impact motivates you more than titles, and if you want hard problems, real ownership, and teammates who love winning, building, and doing it together. The bar is high. The pace is real. We're building for what's next, for humans and agents. Recent recognition: Forbes' America's Best Startup Employers 2026 . 2nd in Crypto Services on Fortune's inaugural Crypto 100 , The Sunday Times Best Places to Work two years running. Research has shown that women are less likely than men to apply for this role if they do not have experience in 100% of these areas. Please know that this list is indicative, and that we would still love to hear from you even if you feel that you are only a 75% match. Skills can be learned, diversity cannot. ______________________________________________________________________________________________________ Locations Supported 🌍 London, UK Relocation available: No Work pattern: Hybrid: our teams meets in the office ~1-2 days a week About the Opportunity Every transaction we process requires a real-time decision. Declining a legitimate transaction leaves a customer stuck at the point of purchase, while approving a fraudulent one carries a direct cost. This role owns the decisioning system and underlying platform. From the serving path and feature infrastructure to the underlying models and the machinery required to make safe, live updates. You will continuously improve the platform and our day to day workflows, rather than treating these as secondary projects. As a Staff Machine Learning Engineer, you will hold a hands-on technical position. You will be part of a team that builds, ships, and maintains the entire machine learning lifecycle. Our main focus is fraud detection and prevention, an adversarial domain where opponents constantly adapt and feedback arrives in the form of financial impact. Alongside, this we build broader capabilities to enable machine learning across Moonpay. Lead through ambiguity Turn vague problems into well-defined solutions and bring people with you. Set the technical bar through rigorous reviews, clear standards, and lasting engineering habits. Build and scale the platform Develop feature infrastructure across batch, near-real-time, and in-request paths, managing specific freshness budgets for each. Maintain alignment between training and serving to ensure models behave in production exactly as they did offline. Integrate feedback loops to capture every decision and its outcome, including blocked transactions where results are counterfactual. Scale the platform as volume and model complexity grow, ensuring operational load remains manageable. Decide in real time Own the services that score transactions in-flight, inside a hard latency budget Design the degraded paths: what we answer when the model can't, and who agreed that policy Ship safely, continuously Mature the replay, shadow and staged-rollout tooling until changing a live model is routine and reversible Own models across their lifecycle, from training through to retirement, and catch decay long before losses confirm it About You Must-have experience and skills Real-time serving.

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