Intuitive's Platform Engineering team is looking to architect and build a next-generation automation platform that bridges digital products with robotic systems, aiming to improve software reliability, faster iteration, and enhanced visibility for web and mobile applications.
Requirements
- 8+ years of hands-on experience in DevOps, release engineering, configuration management, or related software development discipline.
- Proven track record building and operating CI/CD pipelines for distributed systems (e.g., Jenkins, GitLab CI, CircleCI).
- Expert in Python (scripting, automation frameworks) and comfortable authoring build/test tooling.
- Proven experience with containerization and orchestration (Docker, Kubernetes) and integrating containers into CI/CD workflows.
- Demonstrated ability to write and maintain infrastructure-as-code (Terraform, CloudFormation, or equivalent) and integrate IaC into automated pipelines.
- Solid knowledge of Linux (Ubuntu, RHEL) and Windows server environments, including configuration and troubleshooting.
- Experience with cloud platforms (AWS and/or Google Cloud) for deploying build servers, data pipelines, or test infrastructure.
Responsibilities
- Architect, implement, and maintain end-to-end build, test, and deployment pipelines for digital products and AI/ML workflows, all integrated with robotic platforms.
- Develop reusable automation frameworks and scripts (Python, shell, etc.) to reduce manual effort, minimize errors, and accelerate delivery.
- Design and extend Jenkins, GitLab CI, or similar tooling to enforce consistent quality gates (static insights, unit tests, integration tests) before every release.
- Act as a true systems engineer and drive efforts to integrate hardware-in-the-loop (HIL) testing of our edge devices with simulator-based test suites that validate edge-device interactions with robotic systems and cloud-native solutions.
- Build processes to ingest test results and telemetry data into a scalable cloud data pipeline (AWS/GCP) for real-time insights and visualization of software reliability metrics.
- Collaborate with embedded and digital teams to ensure consistent interfaces between edge devices, cloud services, and client-facing applications.
- Leverage AI/ML tools, data analytics, and predictive modeling to enhance pipeline reliability, automate anomaly detection, optimize test coverage, and enable data-driven decision making through real-time monitoring and actionable insights derived from pipeline and test execution data.
Other
- Excellent communication skills—able to lead technical discussions, document standards clearly, and mentor peers.
- Proven ability to learn complex systems quickly and drive creative yet practical solutions from high-level requirements.
- Serve as a technical mentor to DevOps engineers, SDETs, and SWE peers—conduct design reviews, share best practices, and encourage incremental improvements.
- Facilitate cross-team workshops or design sessions that align hardware, software, and QA roadmaps; translate complex requirements into automated test strategies.
- Act as the primary point of contact for integration questions—help unblock teams by clarifying dependencies, reviewing architecture proposals, and advocating for automation first.
