Software Engineer, Rhoda Platform

About the company

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.

Your tasks

We're looking for a Software Engineer to help build and maintain the broad software foundation that keeps our robots running. You'll work across the full onboard stack — from networking and communication infrastructure to perception and control integration, data collection pipelines, and the tooling that lets us continue to develop and improve our robots in the field.

• Build and maintain the networking and communication infrastructure that connects onboard robot systems to each other, to the cloud, and to teleoperation and monitoring systems
• Own data collection pipelines that capture sensor, telemetry, and behavioral data from the robot and reliably upload it to cloud storage for model training
• Build and maintain telemetry and observability systems — giving the team real-time visibility into robot health, system state, and field performance
• Develop internal tooling that accelerates robot software development: deployment pipelines, logging infrastructure, debugging tools, and field testing utilities
• Productize the Rhoda Platform to enable efficient integration with vendor robotic hardware platforms
• Support bring-up and field testing of new hardware revisions, debugging issues across the full onboard software stack
• Collaborate closely with ML, perception, and control engineers to ensure onboard systems meet the data quality and reliability requirements of model training and deployment

Why This Role

• Your work is the connective tissue of the robot — the networking, monitoring, data, and tooling infrastructure you build directly determines how fast the team can develop, debug, deploy and improve our robots in the field
• Work across the full onboard stack alongside perception, control, and ML engineers on a platform that is pushing the frontier of what robots can do
• High ownership in a small, fast-moving team where your contributions shape the development velocity and reliability of the next generation of robots

Requirements

• For experienced hires: 2+ years of experience in robotics software engineering, embedded systems, cloud software, or a closely related field; For new graduates: solid educational background in Computer Science, Electrical Engineering, Robotics, or related fields
• Passion about a fast-moving & collaborate working environment
• Strong software engineering fundamentals in C++, Python, or Rust
• Experience with networking, IPC, and communication protocols for hardware-software systems (e.g., gRPC, DDS, MQTT, WebSockets)
• Familiarity with cloud storage and data upload pipelines (e.g., AWS, GCP, S3, GCS)
• Experience with ROS/ROS2 or similar robotics middleware in production or research contexts
• Ability to debug across the full stack — from driver-level issues to application behavior to failures in a distributed system, on live hardware
• Excellent engineering principles and high standard on work deliverables
• Comfort working directly with physical hardware in fast-moving, ambiguous environments

Nice to Have (But Not Required)

• Experience building telemetry and observability systems for embedded or edge platforms
• Familiarity with perception pipelines or control systems and how they interact with the broader robot stack
• Background in streaming systems or low-latency data transport for real-time robot operation
• Experience with over-the-air (OTA) software updates and deployment infrastructure for robot fleets
• Exposure to learned perception or control models and their integration into real-time robot stacks
• Prior work on early-stage hardware programs (prototype or pre-production robots)