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AI Platform Reliability Lead

James Yamamoto

Full-time · Job · New_York

About the role

We're building the next generation of AI-powered drug discovery infrastructure, and the model-serving layer our research teams depend on needs a dedicated owner. Fast-moving team, high-stakes workloads, and zero patience for flaky pipelines. You'll take end-to-end ownership of uptime and performance across our inference and training stack, from GPU scheduling to rollout safety. When a scientist queries a model at 2am, it needs to just work. This is a high-trust role with a short reporting line to engineering leadership. You'll set the standard for what 'production-grade' means at a biotech AI company.

Responsibilities

  • Own uptime and SLOs for the model-serving and training infrastructure
  • Design rollout and rollback procedures for new model versions
  • Partner with research scientists to translate reliability needs into infra work
  • Build monitoring and alerting for GPU cluster health

Requirements

  • 5+ years running production ML infrastructure at scale
  • Deep comfort with Kubernetes and container orchestration
  • Experience with MLOps tooling such as MLflow or similar
  • Comfortable being on-call for critical systems

Benefits

  • Fully remote position
  • Equity in an early-stage biotech AI company
  • Health and dental insurance
  • Home office stipend

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python kubernetes docker aws mlflow

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python kubernetes docker aws mlflow

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