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Senior ML Platform Engineer
Cascadia Inference Co
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 habilidades 1 herramienta
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Modalidad Hybrid
Ubicación Seattle, WA, United States · Hybrid
Compensación $175k–$220k + equity Nivel de experiencia Beneficios Relocation, wellness. Cómo aplicar One cost win you delivered on inference/training.
Publicada 20 may 2026
Actualizada 3 jul 2026 Cascadia Inference Co is hiring a Senior Senior ML Platform Engineer on the ML platform team (Hybrid · Seattle, 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.
Cascadia Inference Co powers batch and realtime ML for SaaS analytics products used by Fortune 500 teams. Our platform blends Spark/Ray workloads with GPU bursts and humane FinOps dashboards.
Hiring a Senior ML Platform Engineer in Seattle (hybrid) to deepen training orchestration and feature freshness guarantees.
What you will do
Evolve orchestration for large-scale offline training jobs with preemptible fleets and checkpointing discipline. Improve feature store ingestion SLAs and backfill ergonomics for data scientists. Instrument cost attribution by team, workload, and model version; drive optimisation programs. Collaborate with SRE on incident tooling for ML pipelines distinct from classical services. Prototype next-gen tooling (workflow engines, policy-as-code for compute quotas). What we look for
5+ years building ML or data platforms in cloud-native environments. Strong Python and Kubernetes; comfort with Spark or Ray operational concerns. Systems thinking and measured rollouts; customer empathy for DS personas. US work authorisation. Nice to have
Experience with GPU schedulers (Volcano, Run:ai) or Slurm bridges. Prior FinOps or unit economics modelling for infra teams. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
Kubernetes
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWS
Cloud primitives for training, storage, IAM, and cost-aware ML platform & MLOps deployments.
Docker
Containerized services and reproducible dev environments for ML and LLM workloads.
✓ You can show evidence in your application: One cost win you delivered on inference/training. ✓ Several of these show up in recent shipped work—not only on your CV: Python, Kubernetes, AWS, Docker, and PostgreSQL. ✓ You have owned training, deployment, observability, and cost control in production: debugging live issues, running postmortems, and iterating from real user or business signals. ✓ You are comfortable with the hybrid rhythm (Hybrid · Seattle, 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: $175k–$220k + equity, benefits in At a glance) and your expectations align. Lo que pide la empresa
One cost win you delivered on inference/training.
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.
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Título
Senior ML Platform Engineer
Aplicar Pensado para equipos que contratan personas que trabajan con IA, no solo alrededor.
Los candidatos ven habilidades y herramientas al inicio; recibes candidaturas estructuradas en un solo panel.
Llega a profesionales de IA que buscan roles con expectativas basadas en pruebas.
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