Level

Together AIPosted 1mo ago

Staff Software Engineer, Inference / Compute Infrastructure Engineering London or Amsterdam

Staff Software Engineer, Inference / Compute Infrastructure Engineering London or Amsterdam at Together AI scores 95 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

London & Amsterdamsenior

AI in this role

Build and operate Kubernetes-native control plane software to provision and scale GPU inference fleets.

cursorelevenlabsdecagonkubernetesdockerlinuxgpu
fine-tuningkubernetesinfrastructuredistributed-systemsplatform-engineeringdevops

About the Role

We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered.

A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship.You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production.

Responsibilities

  • Build the provisioning state machine: design and implement the software that models the full lifecycle of a physical host from discovery, inference bring-up to GPU driver/CUDA stack, health validation, and decommission/RMA — as explicit, versioned states and transitions.
  • Build the self-service API: design declarative APIs and a control plane so the inference team can request, scale, and tear down inference clusters with one API call — no ticket, no human in the loop.
  • Automate self-healing: detect degraded or failed nodes, drain them safely, trigger repair or replacement, and reintroduce healthy capacity into the pool automatically.
  • Own reliability of the pipeline: idempotency, retries, rollback, and drift detection so the provisioning system is as dependable as any other production service.
  • Partner with the inference/ML platform team: understand the cluster shapes they need — topology, interconnect, scheduling constraints — and encode them as first-class abstractions in the platform.
  • Engineer it like software: strong typing, automated tests, code review, versioning, and CI/CD for infrastructure code — this is a product, not a collection of Ansible playbooks.

Requirements

Core requirements (all levels):

  • Strong software engineering background in Go, Python, Rust, or similar — you write and test real software for a living.
  • Experience with durable workflow orchestration tools such as Temporal, Cadence, or equivalent to run long-lived, manifest-driven workflows that survive failures and resume mid-execution.
  • Experience building software control planes or orchestration systems that model state and reconcile it over time (e.g., Kubernetes controllers/operators, custom reconciliation loops, workflow engines).
  • Experience with event-driven systems — designing and building software around message queues, event streams, or pub/sub (e.g., Kafka, NATS, SQS) rather than polling or cron-driven scripts.
  • A product mindset. You’ve built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship.

Nice to have:

  • Exposure to bare-metal provisioning (PXE/iPXE, Redfish/IPMI, BMC) and/or networking fundamentals (VLANs, BGP, fabric design), or GPU/accelerator infrastructure.
  • Experience with GPU cluster software stacks (NCCL, CUDA, InfiniBand/RoCE).
  • Prior work at a hyperscaler, GPU cloud, or datacenter-scale infrastructure organization.
  • Systems programming in Rust or Go.

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.

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.

Please see our privacy policy at https://www.together.ai/privacy.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine TuningKubernetesInfrastructureDistributed SystemsPlatform EngineeringDevopsCursorElevenlabs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Tell me about a project where kubernetes was part of your work. What did you do?
  3. Tell me about a project where infrastructure was part of your work. What did you do?
  4. Tell me about a project where distributed systems was part of your work. What did you do?
  5. Tell me about a project where platform engineering was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Fine Tuning, Kubernetes, Infrastructure, Distributed Systems, and Platform Engineering. 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.

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