Systems Software Engineer — Marvis Minis & Edge AI

This role has been designed as 'Onsite' with an expectation that you will primarily work from an HPE office.

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next.

We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

The future of networking is autonomous and AI-driven. HPE Mist Networking is building that future through Marvis Minis—a digital twin framework that runs directly on access points to continuously validate network health and feed data into our self-driving network engine.

This team owns the Minis platform end-to-end: the embedded agent, cloud data pipelines, SLE classifiers, and integrations with Marvis Actions and the Large Experience Model. You’ll work across this stack — contributing to features, debugging cross-layer issues, and growing your expertise in both embedded and cloud systems.

Requirement:
On-site in Cupertino 2+ days a week.

• Develop and test Minis features across embedded (AP firmware, switches, gateways, via sandboxed test execution) and cloud (data pipelines, SLE classifiers, REST APIs).
• Write and maintain Minis tests— network and application validation tests such as DNS, DHCP, ping, MTR that run on networks like APs, switches, gateways and report results to the cloud via Kafka.
• Debug cross-layer issues— troubleshoot problems that span AP firmware, cloud services, and data pipelines (e.g., why a downloadable mini fails on specific AP models, why SLE classifiers show incorrect data).
• Contribute to cross-platform expansion— help extend Minis to switches and WAN edge devices, working with peer dev teams.
• Build and improve cloud services— work on Storm topologies, Airflow DAGs, Redis caching, Elasticsearch queries, and Kafka consumers that process millions of Minis test results.
• Participate in production operations— monitor rollouts of features and respond to customer-reported issues.
• Collaborate with senior engineers, the data science team, QA, and firmware teams.

• 4+ years of professional software engineering.
• 2+ years of hands‑on Go, C, or Python development, having written, reviewed, tested, and shipped code in at least two of these languages.
• 2+ years working on Linux-based systems. Comfortable working daily in a Linux environment, writing shell scripts, navigating the file system, using debugging tools (gdb, strace, tcpdump), managing processes, and understanding file permissions and basic networking configuration.
• Networking fundamentals:
  Understanding of TCP/IP, DNS, DHCP, and HTTP sufficient to explain client IP assignment, hostname resolution, HTTP requests, and interpret packet captures or trace route output.
• 1+ year of Cloud or distributed systems experience with message queues (Kafka, MQTT), stream processing (Storm, Flink), REST API development, or containerized deployments (Docker).
• 1+ year of Version control and CI/CD using Git-based workflow with pull requests, code reviews, and CI pipelines.
• Education:
  
  BS in Computer Science, Electrical Engineering, or a related technical field.

• Experience with embedded Linux development—cross-compilation, on-device debugging, resource-constrained environments.
• Exposure to data pipeline tools:
  Apache Storm, Airflow, Redis, Elasticsearch.
• Familiarity with AI/ML concepts—model inference, data preprocessing, or signal processing.
• Experience with wireless networking, AP hardware, or IoT protocols.
• Experience with production monitoring, alerting, and incident response.
• Familiarity with Python data tools (Pandas, Num Py) or JSON data processing.