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Open nowPosted 181 days ago

Software Engineer, Inference Infrastructure

Rhoda AI45 open roles

Pay
$175,000 – $280,000 a year
Where
Mountain View
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Your applicationOpen nowSoftware Engineer, Inference InfrastructureRhoda AI · Mountain View

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market. Rhoda AI postings stay open a median of 27 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.8%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.6%30 days
  6. 59.1%60 days
This job: posted 181 days ago

Rhoda AI median: 27 days open

The posting

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

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