Engineering Manager, Kubernetes Bare Metal Infrastructure

Posted Yesterday
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San Francisco, CA, USA
In-Office
300K-350K Annually
Senior level
Artificial Intelligence • Information Technology
The Role
Lead the engineering team responsible for operating Kubernetes on large-scale bare-metal GPU infrastructure. Set platform direction, automate GPU node provisioning and lifecycle management, improve scheduling, networking, observability, reliability, and utilization, and establish SLOs and incident practices. Hire and develop infrastructure engineers, manage roadmap delivery and on-call operations, and collaborate with data center, networking, hardware, and product teams on capacity planning and new hardware deployments.
Summary Generated by Built In
About The Role

We are hiring an Engineering Manager/Tech Lead Manager to lead the team that runs Kubernetes on bare-metal GPU infrastructure at Together AI. You will own how thousands of GPUs across our data centers are provisioned, orchestrated and kept healthy for the customers training, fine-tuning and serving models on our platform.

Together AI is the AI Native Cloud: a full-stack platform for running open and custom generative AI models, from GPU clusters to serverless inference. Our research and engineering teams contribute widely used open-source work such as FlashAttention, and our infrastructure runs at the frontier of GPU hardware.

This team sits at the base of that stack. Every training run, inference endpoint and customer cluster depends on the reliability, utilization and speed of the bare-metal Kubernetes layer you will lead.

Responsibilities

Own the bare-metal Kubernetes platform

  • Set the technical direction for how we run Kubernetes on bare metal across multiple data centers and regions.
  • Drive automated, zero-touch provisioning of GPU nodes, from firmware and OS imaging through cluster join and validation.
  • Own cluster lifecycle at scale: upgrades, capacity expansion, node health checks, automated remediation and decommissioning.
  • Make GPU scheduling, topology-aware placement and high-performance networking (InfiniBand, RoCE, RDMA) first-class in the platform.

Drive reliability and efficiency

  • Define and meet SLOs for cluster availability, node bring-up time and GPU utilization.
  • Invest in observability across hardware, networking, Kubernetes and workloads so failures are found before customers see them.
  • Cut the time from racked hardware to customer-ready capacity.

Lead and grow the team

  • Hire, manage and develop a team of infrastructure and platform engineers, setting a high bar for technical depth and operational ownership.
  • Run the team's planning, prioritization and delivery, balancing roadmap work against fleet growth and production support.
  • Build a healthy on-call culture: sustainable rotations, blameless postmortems and follow-through on remediation.
  • Work with data center operations, networking, hardware vendors, inference, training and product teams to plan capacity and launch new hardware generations.
  • Represent the platform's needs and trade-offs to leadership, and turn customer requirements into a clear roadmap.
Requirements
  • 8+ years in infrastructure, SRE or platform engineering, including 2+ years managing engineers.
  • Deep hands-on experience running Kubernetes in production at scale, ideally on bare metal rather than only managed services.
  • Solid grounding in Linux systems, networking and hardware: kernels, drivers, BIOS/BMC, PXE boot and out-of-band management.
  • Experience building automation for fleet provisioning and lifecycle with tools such as Cluster API, Metal3, Ironic, MAAS, Ansible or Terraform.
  • A track record of owning production systems with on-call, SLOs and incident response.
  • Strong technical judgment: you can review designs, debug alongside the team and make clear trade-offs.
  • Clear communication with engineers, leadership and external partners.

Nice to have

  • Experience operating large NVIDIA GPU clusters (H100, H200, B200, GB200) and the NVIDIA GPU and Network Operators, DCGM and NCCL.
  • Familiarity with InfiniBand, RoCE, SR-IOV and high-performance storage for AI workloads.
  • Experience with Slurm, Kueue, Volcano or other batch schedulers for training workloads.
  • Contributions to Kubernetes or related CNCF projects.
  • Experience at an AI lab, GPU cloud or hyperscaler.
About Together AI

Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $300,000 -350,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Application Policy 

Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Skills Required

  • 8+ years of experience in infrastructure, SRE, or platform engineering
  • 2+ years of experience managing engineers
  • Deep hands-on experience running Kubernetes in production at scale, preferably on bare metal
  • Strong knowledge of Linux systems, networking, and hardware, including kernels, drivers, BIOS/BMC, PXE boot, and out-of-band management
  • Experience automating fleet provisioning and lifecycle management using tools such as Cluster API, Metal3, Ironic, MAAS, Ansible, or Terraform
  • Experience owning production systems with on-call responsibilities, SLOs, and incident response
  • Strong technical judgment, including design review, debugging, and trade-off decisions
  • Clear communication with engineers, leadership, and external partners
  • Experience operating large NVIDIA GPU clusters, including H100, H200, B200, or GB200 systems
  • Familiarity with InfiniBand, RoCE, SR-IOV, and high-performance storage for AI workloads
  • Experience with Slurm, Kueue, Volcano, or other batch schedulers
  • Contributions to Kubernetes or related CNCF projects
  • Experience at an AI lab, GPU cloud, or hyperscaler

Together AI Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Together AI and has not been reviewed or approved by Together AI.

  • Fair & Transparent Compensation — Pay is considered competitive for the company’s stage, with especially strong offers for technical and research roles. A posted AI researcher range reaching into the high-$200Ks alongside equity and benefits reinforces the overall pay positioning.
  • Equity Value & Accessibility — Equity is commonly included alongside base salary, giving employees ownership exposure tied to company growth. Total compensation is often framed around salary plus equity for specialized roles.
  • Flexible Benefits — Job listings describe a flexible time‑off policy and hybrid or remote work options. Commuter perks and regular team events are also cited as part of the package.

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The Company
HQ: Surry Hills
84 Employees
Year Founded: 2022

What We Do

Together AI is a research-driven artificial intelligence company. We contribute leading open-source research, models, and datasets to advance the frontier of AI. Our decentralized cloud services empower developers and researchers at organizations of all sizes to train, fine-tune, and deploy generative AI models. We believe open and transparent AI systems will drive innovation and create the best outcomes for society

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