Rol IA · Oferta abierta
ML Engineer — spend & fraud
Ramp
- Tiempo completo
- Senior
- Risk
- Hybrid
- 28 West 23rd Street, New York, NY 10010, United States · Hybrid
- New York, United States
- $170k–$225k + equity
De un vistazo
Cómo trabajarás, dónde y fechas clave del anuncio.
- Empleo
- Tiempo completo
- Modalidad
- Hybrid
- Ubicación
- 28 West 23rd Street, New York, NY 10010, United States · Hybrid
- Compensación
- $170k–$225k + equity
- Nivel de experiencia
- Senior
- Equipo
- Risk
- Beneficios
Sobre el rol
Qué busca el equipo y cómo encaja el puesto.
Ramp is hiring a Senior ML Engineer — spend & fraud on the Risk team (Hybrid · New York, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Ramp automates finance operations with ML on spend, fraud, and policy compliance.
ML Engineer — risk (NYC): real-time features, LLM document parsing, and analyst copilots.
What you will do
- Deliver production-grade fintech risk ML with measurable quality, latency, and cost targets.
- Build offline evals and online monitoring before scaling to full traffic.
- Collaborate with product, design, legal, or domain experts on safe rollout.
- Write clear RFCs, runbooks, and postmortems for cross-team learning.
What we look for
- Multiple shipped ML/LLM features—not coursework-only portfolios.
- Strong Python, SQL, and experimentation discipline.
- Clear communication for remote or hybrid stakeholders.
- Work authorization for the listed country.
Habilidades que buscamos
Qué implica cada capacidad en este rol — no solo una lista de palabras clave.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
- SQL
Herramientas y stack
Plataformas y productos mencionados para este rol (APIs, IDEs, BBDD vectoriales, CRM, diseño, etc.).
- SnowflakeTool
Regular use of Snowflake in the LLM & generative AI stack—configuration, debugging, and team conventions.
Encajas muy bien si…
Adaptado a esta oferta — úsalo para comprobar tu encaje antes de aplicar.
- ✓You can show evidence in your application: Fraud or document ML with production metrics.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, LangChain, SQL, and Snowflake.
- ✓You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · New York, United States)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
- ✓You read the full listing (comp: $170k–$225k + equity, benefits in At a glance) and your expectations align.
Aplicar
Solo cuentas candidato: añade tu titular y bio en , y opcionalmente un enlace o PDF de CV para que los reclutadores vean tu CV completo. Luego escribe un breve pitch — las candidaturas usan el correo de tu cuenta.
Lo que pide la empresa
Fraud or document ML with production metrics.
Inicia sesión con cuenta candidato para aplicar
Regístrate, añade titular y bio; luego podrás adjuntar enlace o PDF de CV en Mi cuenta antes de aplicar. Así las candidaturas quedan ligadas a tu perfil.
Sugerencias
Talento IA que podría interesarte
Perfiles ordenados por solape con habilidades y herramientas de este rol — útil si contratas equipo o comparas candidatos.
Aún no hay coincidencias cercanas en el directorio — prueba con tus filtros.
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