Software Engineer, Inference Infrastructure

Role details

At 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 a Software 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 Do - Design and operate large-scale infrastructure to run model workloads across cloud and on-prem environments - Build and maintain Kubernetes-based deployment pipelines for managing distributed ML workloads - Own resource scheduling and orchestration across GPU clusters — optimizing utilization, workload balancing, and cost-performance tradeoffs - Integrate and manage ML frameworks and model serving systems (e.g., Triton, Ray Serve, TorchServe) across research and production use cases - Build tooling for model deployment, versioning, and observability to support fast iteration cycles - Contribute to the reliability and scalability of the infrastructure stack as model complexity and deployment footprint grow What We're Looking For - 3+ years of experience in ML infrastructure, MLOps, or distributed systems - Strong proficiency with Kubernetes and containerized deployment pipelines - Experience with GPU orchestration and resource scheduling across large distributed jobs - Experience with cloud providers (e.g., AWS, GCP) and hybrid cloud/on-prem infrastructure - Familiarity 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 stack Nice 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 scheduling - Experience with edge/cloud hybrid deployment patterns and the latency constraints that come with them - Familiarity with on-robot or embedded inference environments - Experience with large-scale cluster topology and scheduling systems (e.g., SLURM, Ray, Volcano) Why This Role - Own 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 world - Be part of building the infrastructure stack for one of the most technically ambitious robotics companies in the world