Rol IA · Oferta abierta

Staff ML Platform Engineer — inference at scale

Databricks

Tiempo completo
Staff
Model serving
Hybrid
160 Spear Street, San Francisco, CA 94105, United States · Hybrid
San Francisco, United States
USD 190k–260k + equity

Este anuncio está pensado para talento nativo en IA: habilidades y herramientas claras para saber si encajas antes de aplicar — y para reducir descartes por desajuste.

4 habilidades3 herramientas

De un vistazo

Cómo trabajarás, dónde y fechas clave del anuncio.

Empleo: Tiempo completo
Modalidad: Hybrid
Ubicación: 160 Spear Street, San Francisco, CA 94105, United States · Hybrid
Compensación: USD 190k–260k + equity
Nivel de experiencia: Staff
Equipo: Model serving
Beneficios

Sobre el rol

Qué busca el equipo y cómo encaja el puesto.

Databricks is hiring a Staff Staff ML Platform Engineer — inference at scale on the Model serving team (Hybrid · San Francisco, United States). This is a hands-on role in ML platform & MLOps—not a generic “AI enthusiast” post.

You will own training, deployment, observability, and cost control 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.

Databricks runs the Lakehouse and Mosaic AI platform for model training and serving.

Staff ML Platform Engineer: inference at scale—SLOs, cost, and multi-tenant model serving.

Missions

Design serving paths for LLMs and classical models with autoscaling and observability.
Reduce cost per token / prediction without breaking latency SLOs.

Stack & environment

Python, Kubernetes, Spark, Docker, PyTorch.

What we look for

Staff-level ML platform or serving experience; cloud-native ops.

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.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
Machine Learning

Herramientas y stack

Plataformas y productos mencionados para este rol (APIs, IDEs, BBDD vectoriales, CRM, diseño, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

Encajas muy bien si…

Adaptado a esta oferta — úsalo para comprobar tu encaje antes de aplicar.

You can show evidence in your application: Serving stack for LLMs or classical ML with SLOs and cost controls.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, Machine Learning, MLOps, and Kubernetes.
You have led ML platform & MLOps initiatives across teams—setting technical direction, review standards, and rollout plans others could follow.
You are comfortable with the hybrid rhythm (Hybrid · San Francisco, 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: USD 190k–260k + 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

Serving stack for LLMs or classical ML with SLOs and cost controls.

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.

Iniciar sesión Crear cuenta candidato

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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Staff ML Platform Engineer — inference at scale

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