About the Role
Many believe that as long as a robot is “smart” enough, deploying it is easy—but the real world is unpredictable and unforgiving. Landing breakthrough AI research into the field requires deep investment in infrastructure and applied research. As our client scales their deployed footprint, the bottleneck is no longer whether a robot can perform a task once; it is whether they can deploy, monitor, debug, and improve robots across many environments without every site becoming a one-off engineering project.
Your job is to turn that messy, high-friction reality into durable engineering infrastructure. In this cross-domain role, you will bridge applied research on model stability, on-robot software, and the off-board cloud systems and tooling that keep a fleet running.
Key Responsibilities
- Build the Deployment Platform: Design and ship internal tools and highly reliable software infrastructure for site bring-up, robot configuration, fleet monitoring, remote diagnostics, data capture, failure triage, deployment checklists, and operational dashboards.
- Partner with Operations: Embed with the field ops team to map real-world conditions, isolate which hardware/software failures matter most, and turn manual operational workflows into automated, reliable software.
- Close the Loop with AI Research: Build data pipelines that seamlessly route useful edge-case data and failure telemetry from the fleet back to the AI research teams to facilitate model training and cross-environment performance evaluation.
- Applied Research & Model Stability: Participate in applied engineering research focused on ensuring model-level stability and deterministic consistency when deploying complex deep learning models into unpredictable, real-world environments.
- End-to-End Infrastructure Architecture: Design systems that bridge on-robot application software with large-scale off-board computing, cloud operations, and continuous integration pipelines.
Required Qualifications & Technical Stack
- Education: Bachelor’s degree or higher in Computer Science, Computer Engineering, Robotics, or a related technical field.
- Experience: At least 3+ years of professional experience as a versatile software engineer, with a strong track record spanning backend systems and broad application-level software.
- Systems Architecture: Deep, first-principles understanding of systems design, software design patterns, and application development—you know how systems work under the hood, not just how to connect APIs.
- Core Ecosystem: Proficient in the Python ecosystem.
- Cloud & Edge Infrastructure: Proven experience building robust software that bridges cloud backend services with edge devices, physical hardware, or massive data collection systems. Strong capability with containerization and orchestration (Docker, Kubernetes) and cloud providers (AWS, GCP, or Azure).
- Mindset: Highly adaptable to changing stacks, comfortable with ambiguity, and possessing a deep interest in operational pain. You are driven to turn heroic on-site troubleshooting into permanent engineering solutions.
Preferred Qualifications (Bonus Points)
- Prior experience tackling model stability, consistency, and edge-case handling in live hardware deployments.
- Experience building proprietary internal tooling for fleet operations, autonomous vehicles, or large-scale field systems.
- Familiarity with Unity, C#, and VR development platforms.
- Familiarity with modern web frontend technologies for dashboard visualization.