Чётко описанная роль principal-уровня в AI-инфраструктуре даёт серьёзную техническую ответственность и зарплату выше медианы, хотя широкий диапазон оплаты и отсутствие конкретного города оставляют важные детали неясными.
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Большой разбросСовременный стекШирокая ответственностьAI-инфраструктураВыше медианы
Оценка от Hirify AI
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Текст:
Hirify AI/Оригинал
TL;DR
Principal Observability Platform Engineer (AI): Building and directing an observability platform for GPU clusters, AI workloads, and supporting infrastructure with an accent on metrics, logs, traces, alerting, and scalable data architecture. Focus on designing high-volume ingestion and retention systems, improving failure diagnosis, and embedding observability across SRE, infrastructure, and AI/ML engineering.
Location: US
Salary: $190,000–$300,000 USD base salary per year, with potential bonus, equity, and/or commission eligibility.
Company
Nscale provides high-performance GPU cloud infrastructure for AI startups and enterprise customers.
What you will do
Own the technical strategy and architecture for observability across metrics, logs, traces, and alerting at scale.
Define tooling, data models, ingestion patterns, retention policies, and cardinality-management strategies.
Design platforms that identify systemic gaps, make failures visible, and accelerate diagnosis.
Partner with SRE, infrastructure, and AI/ML teams to embed observability into engineering and operations.
Establish engineering standards, evaluate observability tooling, and lead durable improvements from incident postmortems.
Mentor and technically grow the observability team while raising engineering standards across the organization.
Requirements
8+ years of experience in SRE, infrastructure engineering, platform engineering, or observability-focused roles.
Experience operating observability infrastructure at significant scale and designing for 10x growth.
Deep hands-on experience with a significant subset of Prometheus, Thanos, VictoriaMetrics, Grafana, Loki, Tempo, OpenTelemetry, ClickHouse, or Elastic.
Strong engineering fundamentals and proficiency in Python, Go, or a similar language, with end-to-end ownership of complex systems.
Experience with Kubernetes at scale and Infrastructure-as-Code using Terraform, Ansible, or an equivalent tool.
Ability to architect systems, write and review code, explain trade-offs, and influence teams without direct authority.
Nice to have
Experience with high-volume observability streaming pipelines using Kafka, Vector, Fluent Bit, or similar tools.
Background in AI/ML infrastructure observability, including GPU utilization, training-job visibility, and inference latency.
Familiarity with GPU infrastructure or HPC environments such as Slurm.
Experience defining observability strategy at the organizational level.
Culture & Benefits
Culture centered on innovation, ownership, accountability, openness, and transparency.
Medical, dental, and vision benefits.
Flexible paid time off and parental leave.
Retirement plan participation.
Potential bonus, equity, and/or commission programs in addition to base salary.
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