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Senior Technical Product Manager - Serverless AI

1 hour, 30 minutes ago
Europe, United States, Netherlands
Full-time
Senior
Technical Product Manager
Artificial Intelligence and Machine Learning
Docker Kubeflow Kubernetes Load Balancing Machine Learning TLS
Apply Now
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
View All Jobs 191

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

Apply Now

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