Together AI
Jobs
Junior/Senior or Staff Software Engineer, Inference / Compute Infrastructure Engineering
Together AI
Junior/Senior or Staff Software Engineer, Inference / Compute Infrastructure Engineering
Build production software for automated GPU and inference cluster provisioning. Design declarative APIs, control planes, state machines, reconciliation loops, durable workflows, and self-healing systems that manage hardware from discovery through deployment, operation, repair, and decommissioning. Partner with inference and ML platform teams to model cluster topology and scheduling requirements. Own reliability, testing, CI/CD, deployment, and production operations for the platform.
About the Role
Responsibilities
REMOTE IN INDIA
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.
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.
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 OpportunityTogether 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.
Similar Jobs
.jpeg)
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Develops and controls integrated project schedules for large-scale EPC, industrial construction, semiconductor fab, infrastructure, and related projects. Responsibilities include baseline management, critical path and delay analysis, schedule variance monitoring, recovery planning, contractor schedule validation, progress forecasting, stakeholder workshops, and executive reporting. The role coordinates engineering, procurement, construction, and commissioning teams while using Primavera P6, project controls methods, BI dashboards, and AI tools to support delivery decisions.
Top Skills:
ChatgptClaudeMicrosoft CopilotExcelMicrosoft PowerpointMicrosoft ProjectPower BIPrimavera P6Tableau
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Manages indirect material planning, procurement coordination, inventory accuracy, tooling orders and maintenance, and WIP management for semiconductor assembly and test operations. Drives manufacturing improvements through AI, automation, dashboards, and real-time issue resolution. Maintains process documentation, supports quality and regulatory compliance, and collaborates cross-functionally to improve production flow and achieve operational KPIs.
Top Skills:
Artificial IntelligenceAutomationDashboardsData Visualization SoftwareMesSAP
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Leads industrial engineering for semiconductor assembly and test manufacturing, including capacity and resource planning, labor optimization, operational excellence, cost reduction, manufacturing analytics, and factory systems. Develops capacity models, staffing standards, dashboards, and automation initiatives while supporting budgets and capital planning. Builds and mentors an industrial engineering team and collaborates globally to standardize manufacturing practices and improve efficiency, utilization, cycle time, and cost performance.
Top Skills:
Advanced AnalyticsAIManufacturing SimulationMesExcelPower BISQLTableau
What you need to know about the Bengaluru Tech Scene
Dubbed the "Silicon Valley of India," Bengaluru has emerged as the nation's leading hub for information technology and a go-to destination for startups. Home to tech giants like ISRO, Infosys, Wipro and HAL, the city attracts and cultivates a rich pool of tech talent, supported by numerous educational and research institutions including the Indian Institute of Science, Bangalore Institute of Technology, and the International Institute of Information Technology.
