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)