Job description

About The Role We're looking for a software engineer, reliability-minded but still a builder, to own what happens in production to the Kubernetes-native control plane that provisions and runs our GPU inference fleet. The platform team builds the manifest-driven API and controllers that let the inference team declare what they need, a cluster, a model deployment, a capacity change, and get it reconciled underneath. Your job is to keep that promise true in the real world: triaging InfiniBand issues when prefill and decode nodes stall exchanging KV cache over RDMA, diagnosing hero-model and LoRA-serving faults that don't show up in generic health checks, and extending the automated self-healing system so each new failure mode becomes a permanent fix instead of a recurring page. The core value we're after is keeping the decoupling real even when things break, and keeping the fleet efficient without that efficiency becoming a new source of instability. You'll own SLOs and error budgets for inference serving, lead incident response, and turn what postmortems teach you into actual changes in the controllers and automation, not just updated runbooks. You'll also work the reliability side of capacity efficiency, since a mistuned defragmentation or bin-packing pass is itself a production risk. Strong candidates have hands-on production-reliability experience on large-scale distributed or GPU-serving systems, are comfortable troubleshooting InfiniBand/RDMA and Kubernetes controller/CRD-based platforms, and think about GPU infrastructure, and the tools that keep it reliable, as software to be engineered. A product mindset applies: the on-call experience, the clarity of an alert, and the judgment built into an automated remediation are things you ship and iterate on. Responsibilities Operate the provisioning state machine in production: own the health of the host lifecycle pipeline, discovery, inference bring-up, GPU driver/CUDA validation, health checks, decommission/RMA, triaging stuck or failed state transitions at fleet scale and feeding real failure patterns back into how the states and transitions are designed. Triage and extend automated self-healing: when the node-scanning and remediation platform misses something, a new failure mode, an InfiniBand issue in disaggregated prefill/decode serving, a GPU Xid variant, a hero-model or LoRA-specific fault, diagnose it live, drive the fix to production, and turn it into a new detection/remediation plugin so it's automatic the next time. Own end-to-end reliability of the pipeline: hold the SLOs and error budgets for provisioning and fleet health, lead incident response when idempotency, retry, rollback, or drift assumptions break down in practice, and drive the systemic fix, not just the hotfix. Partner with the inference/ML platform team from the production side: be the one who knows how real cluster topologies, interconnects, and scheduling constraints actually fail in the field, and feed those failure modes back as first-class requirements into the platform's abstractions. Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production, enabling advanced research through awesome engineering. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack. Build tools to give us visibility into our bottlenecks and sources of instability, then design and implement solutions to address the highest-priority issues. Lead reliability cross-functionally: build coalition across infra, ML platform, and product teams around shared reliability priorities, manage stakeholders through incidents and roadmap trade-offs, and represent the reliability point of view in planning so it shapes decisions before they ship, not after an outage. Requirements Experience operating a provisioning or host-lifecycle pipeline in production, comfortable troubleshooting stuck or failed state transitions (discovery, bring-up, GPU driver/CUDA validation, health checks, decommission/RMA) at fleet scale, and translating real failure patterns back into improvements to the state machine’s design. Deep experience owning production reliability for large-scale distributed systems; GPU/ML-serving infrastructure experience is a strong plus. Hands-on experience troubleshooting InfiniBand/RDMA networking issues, ideally in a multi-node training or disaggregated-inference context — comfortable reading IB port counters and subnet manager logs, not just application metrics. Familiarity with the operational realities of multi-tenant model serving (LoRA/adapter-based serving) and with running differentiated SLOs across model pools of different priority. A demonstrated track record of eliminating operational toil through automation and tooling you built yourself, not just process or documentation. Hands-on experience with Kubernetes controller/CRD patterns, reconciliation loops, and node-level health/remediation systems. Comfort with event-driven architectures, backpressure/circuit-breaking design, and treating observability (metrics, logs, traces) as something built into a system, not added after an incident. A software-engineering mindset first: you write and ship production code, and you think about GPU infrastructure as software to be engineered, not a fleet to be manually operated. 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: $250,000 - $320,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. 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 together.ai .

Requirements

Must-haves

A product mindset applies: the on-call experience, the clarity of an alert, and the judgment built into an automated remediation are things you ship and iterate on.
Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production, enabling advanced research through awesome engineering.
Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack.
Build tools to give us visibility into our bottlenecks and sources of instability, then design and implement solutions to address the highest-priority issues.
Deep experience owning production reliability for large-scale distributed systems; GPU/ML-serving infrastructure experience is a strong plus.
Familiarity with the operational realities of multi-tenant model serving (LoRA/adapter-based serving) and with running differentiated SLOs across model pools of different priority.
A demonstrated track record of eliminating operational toil through automation and tooling you built yourself, not just process or documentation.
Hands-on experience with Kubernetes controller/CRD patterns, reconciliation loops, and node-level health/remediation systems.
Comfort with event-driven architectures, backpressure/circuit-breaking design, and treating observability (metrics, logs, traces) as something built into a system, not added after an incident.
A software-engineering mindset first: you write and ship production code, and you think about GPU infrastructure as software to be engineered, not a fleet to be manually operated.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

kubernetesapimlmachine learningai

Platform: greenhouse
Effort: quick

Benefits

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Health Insurance

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