Engineering Manager, Kubernetes Bare Metal Infrastructure

Together AI is hiring an Engineering Manager, Kubernetes Bare Metal Infrastructure in San Francisco, United States. It pays $300k-$350k a year and Level rates it ; you can apply on Together AI.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Cursor, Elevenlabs and Decagon.
Level classifies it as a mid-level role.
Level lists 76 other open roles at Together AI.
The role was posted in the last few days.

AI in this role

CursorElevenlabsDecagon

fine-tuning

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.

How we rate this

Engineering Manager, Kubernetes Bare Metal Infrastructure at Together AI rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Engineering Manager, Kubernetes Bare Metal Infrastructure is an open job at Together AI.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $300k-$350k a year.
The role is based in San Francisco.
The job was posted on November 10, 2026.
Seniority: mid.
Applications are handled by Together AI. Apply on Together AI

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningCursorElevenlabsDecagon

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through how you've used Cursor in your day-to-day work.
What are the limits of Elevenlabs that you've run into, and how did you work around them?
What's a project where you used Decagon hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Fine-tuning, Cursor, Elevenlabs, and Decagon. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

Get your CV reviewed before you apply

Send your CV and the role you want next. Your review arrives by email within 3 days.

Get a free CV review Check your resume with the ATS resume checker