Machine Learning Engineer, Trust & Safety

Vercel·Hybrid - San Francisco, New York Citysenior

En corto

→Ingeniero ML que construye sistemas de detección de abuso a escala global
→Trabajas en producción: pipelines, modelos, infraestructura, monitorización y despliegue
→Destacan las LLM integradas en sistemas de seguridad reales con enfoque en confiabilidad

Fluent English required for collaboration across global teams

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Las preguntas que te van a hacer

1. ¿Cómo has estructurado un pipeline de entrenamiento de ML para detección de abuso en producción?

🔒 +7 preguntas más

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¿Qué piden?

✓5+ años de experiencia en sistemas de producción a gran escala
✓Python, y JavaScript/TypeScript o Go para código en producción
✓Experiencia práctica en infraestructura de ML: pipelines, feature stores, evaluación
✓Integración de LLMs en producción, incluyendo ingeniería de prompts y evaluación
✓Capacidad de llevar modelos de prototipo a sistemas robustos y operativos
✓Trabajo colaborativo con equipos de seguridad, producto e infraestructura

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

PythonJavaScriptTypeScriptGoLLMsPrompt engineeringTraining pipelinesFeature pipelinesServing infrastructureEvaluation frameworks

¿A quién escribirle en Vercel?

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About Vercel: Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. About the Role: We are looking for a Machine Learning Engineer on our Trust & Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office. What you’ll do: • Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability • Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks • Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code • Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration • Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems What you need: • 5+ years of software engineering experience building and operating production systems at scale • Wrote production code in Python, plus JavaScript/TypeScript or Go • Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling • Integrated LLMs into production systems, including prompt engineering and evaluation Bonus if you: • Built an open source ML tool • Worked in the trust and safety, fraud, or abuse prevention domain Compensation & Benefits: • Competitive compensation package, including equity. • Inclusive Healthcare Package. • Learn and Grow - we provide mentorship and send you to events that help you build your network and skills. • Flexible Time Off. • We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed. The San Francisco, CA base pay range for this role is $208,000.00 - $312,000.00. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process. Disclosures: • Privacy: Please review our Job Applicant Pri

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