Inference Infrastructure Engineer

Inference Infrastructure Engineer LocationMountain ViewEmployment TypeFull timeDepartmentSoftwareAt Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.We're looking for an Inference Infrastructure Engineer to help build and operate the systems that power our model deployment stack. You'll be responsible for running large foundation models efficiently and reliably across cloud and on-prem environments, with a focus on resource management, scheduling, and infrastructure scalability.What You'll DoDesign and operate large-scale infrastructure to run model workloads across cloud and on-prem environmentsBuild and maintain Kubernetes-based deployment pipelines for managing distributed ML workloadsOwn resource scheduling and orchestration across GPU clusters — optimizing utilization, workload balancing, and cost-performance tradeoffsIntegrate and manage ML frameworks and model serving systems (e.g., Triton, Ray Serve, TorchServe) across research and production use casesBuild tooling for model deployment, versioning, and observability to support fast iteration cyclesContribute to the reliability and scalability of the infrastructure stack as model complexity and deployment footprint growWhat We're Looking For3+ years of experience in ML infrastructure, MLOps, or distributed systemsStrong proficiency with Kubernetes and containerized deployment pipelinesExperience with GPU orchestration and resource scheduling across large distributed jobsExperience with cloud providers (e.g., AWS, GCP) and hybrid cloud/on-prem infrastructureFamiliarity with ML frameworks (e.g., PyTorch, JAX) and model serving tools (e.g., Triton, Ray Serve, TorchServe)Strong debugging instincts and ownership mentality — comfortable driving issues to resolution across the stackNice to Have (But Not Required)Experience with streaming systems or high-throughput data transport (e.g., Kafka, gRPC, NATS)Background in networking, low-latency systems, or network-aware schedulingExperience with edge/cloud hybrid deployment patterns and the latency constraints that come with themFamiliarity with on-robot or embedded inference environmentsExperience with large-scale cluster topology and scheduling systems (e.g., SLURM, Ray, Volcano)Why This RoleOwn the infrastructure layer that connects our foundation models to real robot behavior — a direct line between your work and what the robot does in the worldBe part of building the infrastructure stack for one of the most technically ambitious robotics companies in the worldApply for this Job