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About the Role

Build the Kubernetes-native control plane for provisioning and running a GPU inference fleet. Design a manifest-driven API and controllers to manage cluster, model deployment, and capacity changes. Develop systems for fleet efficiency, including defragmentation, rebalancing, and GPU utilization improvements. The core goal is to decouple platform users from underlying operational complexity and maximize hardware capacity. This involves building controllers, reconciliation loops, self-service APIs/CLIs, and event-driven health, remediation, and utilization systems.

Responsibilities

Build the provisioning state machine, modeling the full lifecycle of a physical host from discovery to decommission.
Build the self-service API and control plane for requesting, scaling, and tearing down inference clusters.
Automate self-healing by detecting, draining, repairing, and reintroducing nodes.
Own the reliability of the pipeline through idempotency, retries, rollback, and drift detection.
Partner with the inference/ML platform team to understand and encode cluster requirements.
Engineer infrastructure code as a product with strong typing, automated tests, code review, versioning, and CI/CD.

Requirements

Strong software engineering background in Go, Python, Rust, or similar.
Experience with durable workflow orchestration tools (e.g., Temporal, Cadence).
Experience building software control planes or orchestration systems (e.g., Kubernetes controllers/operators).
Experience with event-driven systems (e.g., Kafka, NATS, SQS).
A product mindset with experience building internal platforms or APIs for other engineering teams.
Exposure to bare-metal provisioning or networking fundamentals (nice to have).
Experience with GPU cluster software stacks (nice to have).
Prior work at a hyperscaler, GPU cloud, or datacenter-scale infrastructure organization (nice to have).
Systems programming in Rust or Go (nice to have).

Skills

Kubernetes controller/CRD patterns
Platform API design
GPU scheduling
Capacity efficiency
Developer experience
Go
Python
Rust
Temporal
Cadence
Kubernetes operators
Kafka
NATS
SQS
Bare-metal provisioning
Networking fundamentals
GPU infrastructure
NCCL
CUDA
InfiniBand/RoCE

About the Company

Together AI, the AI Native Cloud, is purpose-built for AI engineers.
Provides high-performance inference, fine-tuning, reinforcement learning, and pre-training at massive scale.
Features a marketplace of leading open models.
Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom.
Serves over 400 trillion tokens per 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.