Senior Technical Product Manager - Serverless AI

1 hour, 30 minutes ago
Full-time
Senior
Artificial Intelligence and Machine Learning
Nebius

Nebius

Nebius enables B2B companies to build local hyperscaling cloud platforms with cost-effective GPUs, InfiniBand network, and 50% less compute cost. They offer managed Kubernetes and a launch-ready business model for innovative cloud solutions.

Internet Software & Services
51-250

Description

  • Co-own the Serverless AI product roadmap across Jobs, Endpoints, and DevPods, with primary ownership of selected areas.
  • Write detailed, technically precise PRDs covering CLI syntax, API contracts, state machines, and billing models.
  • Make build, buy, or defer decisions for capabilities such as autoscaling, multi-node orchestration, HTTPS termination, secret injection, and health checking.
  • Understand the full workload lifecycle and identify bottlenecks across submission, scheduling, provisioning, execution, and cleanup.
  • Evaluate technical trade-offs in container cold start optimization, GPU scheduling, and storage mount performance.
  • Work with engineers on architecture decisions for distributed training, endpoint autoscaling, and fault tolerance.
  • Run customer discovery and feedback sessions with ML engineers and platform teams and turn insights into product actions.
  • Analyze usage data, activation funnels, and churn patterns to improve activation and retention.
  • Define and iterate on pricing, packaging, and tier strategy for Serverless AI.
  • Own technical go-to-market content such as quickstarts, tutorials, reference architectures, and example workloads.

Requirements

  • 3+ years of product management experience in cloud infrastructure, AI/ML platforms, or developer tools.
  • Hands-on experience building and shipping infrastructure or platform products used by developers or ML engineers.
  • Practical understanding of containers, including Docker, image registries, container runtimes, resource limits, and networking.
  • Working knowledge of GPU computing for AI/ML, including GPU types, training versus inference workloads, and tools such as vLLM, TensorRT-LLM, or Triton.
  • Experience shaping developer-facing APIs, CLIs, or SDKs.
  • Experience conducting technical customer discovery conversations that influenced product direction.
  • Ability to reason about workload lifecycle failure modes from submit to cleanup.
  • Understanding of autoscaling trade-offs, including scale-to-zero, warm pools, and scaling metrics such as queue depth, latency, and utilization.
  • Familiarity with inference serving concepts such as batching, model loading, quantization, KV-cache management, and multi-model serving.
  • Familiarity with distributed training concepts such as data parallelism, model parallelism, communication overhead, and checkpointing.
  • Ability to reason about pricing models such as per-second, per-request, and per-token.
  • Experience at a serverless or GPU cloud company is a plus.
  • Hands-on ML engineering background is a plus.
  • Experience with Kubernetes for ML workloads, such as Kubeflow, KServe, or Ray Serve, is a plus.
  • Background in systems engineering, distributed systems, or site reliability engineering is a plus.

Benefits

  • Competitive salary and comprehensive benefits package.
  • Opportunities for professional growth within Nebius.
  • Flexible working arrangements.
  • A dynamic and collaborative work environment that values initiative and innovation.

Interested in this position?

Apply directly on the company website

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