Systems Software Engineer — Marvis Minis & Edge AI

Systems Software Engineer — Marvis Minis & Edge AI

Onsite with 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.

This role will require being 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+ year Go, C, or Python—Hands‑on development in at least two of the following:
  Go, C, or Python. Must have written, reviewed, tested, and shipped code in these languages in a team environment, not just coursework or personal projects.
• 2+ years working on Linux‑based systems. Comfortable working in a Linux environment daily—write shell scripts, navigate the file system, use debugging tools (gdb, strace, tcpdump), manage processes, and understand file permissions and basic networking configuration.
• Networking fundamentals:
  Understanding of TCP/IP, DNS, DHCP, and HTTP—sufficient to explain how a client obtains an IP address, resolves a hostname, and makes an HTTP request, and to interpret packet captures or trace route output when debugging issues.
• 1+ year of Cloud or distributed systems: experience with at least one of: message queues (Kafka, MQTT), stream processing (Storm, Flink), REST API development, or containerized deployments (Docker).
• 1+ year of Version control and CI/CD:
  Comfortable working in a Git‑based workflow with pull requests, code reviews, and CI pipelines in a team setting for at least 1 year.
• 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…