Why join Upscale AI
Upscale AI is building the high-performance infrastructure powering the next generation of artificial intelligence. Backed by over $300M in funding and rapid global adoption, we are scaling systems designed for the world’s most demanding AI workloads.
We focus on first-principles engineering across silicon, systems, and networking—where performance, scale, and execution are critical. Our team is talent-dense and high-performing. We value ownership, technical rigor, and speed, and we offer the opportunity to work on foundational problems with immediate, real-world impact.
If you’re looking to do high-impact work, move fast, and help define the infrastructure behind the future of AI—Upscale AI is where you can produce meaningful work at the frontier—and operate at a high standard.
We are looking for a Senior Staff Engineer, Quality Assurance with 12+ years of experience who is passionate about building reliable networking software and solving challenging infrastructure problems. This role combines software engineering, networking, system validation, and customer deployment experience to help deliver high-performance networking solutions for modern AI infrastructure.
As part of the AI Networking team, you'll build software, develop automation, validate networking platforms, troubleshoot complex system issues, and work closely with engineering teams and customers to ensure successful deployments. You'll have the opportunity to work on technologies powering next-generation GPU clusters and AI data centers while learning from experienced engineers in a fast-paced startup environment.
Responsibilities
Design, develop, and maintain software supporting networking products, infrastructure services, and deployment automation.
Develop automated tests and validation frameworks covering functionality, regression, performance, scale, and reliability.
Develop QoS Test plans, test strategies and tests to qualify Networking ASICs and Networking Systems
Develop debugging tools, for Traffic monitoring and Performance measurements.
Debug issues across software, Linux systems, networking stacks, and distributed infrastructure.
Be a point of contact for customer deployments, integration testing, proof-of-concepts, and field issue resolution.
Build tools that improve deployment efficiency, observability, telemetry, and automated testing.
Collaborate with software, infrastructure, QA, and product teams to identify root causes and deliver robust solutions.
Contribute to backend services, APIs, orchestration components, and infrastructure automation.
Document technical findings and communicate effectively with engineering teams and customers.
Qualifications
12+ years of experience in Software Engineering, Infrastructure Engineering, QA Engineering, Networking, or a related technical field (strong internships or equivalent project experience are welcome).
Strong programming skills in Python, Go, or a similar language.
Solid understanding of Linux, TCP/IP networking, routing, switching, VLANs, and network troubleshooting.
Experience with software development, test automation, CI/CD, or system validation.
Strong debugging, analytical, and problem-solving skills.
Excellent written and verbal communication skills.
Ability to thrive in a collaborative, fast-paced startup environment with a strong sense of ownership.
Preferred Qualifications
Exposure to AI infrastructure, AI backend networking, or modern data center networking.
Familiarity with AI data center networking concepts such as low-latency Ethernet fabrics, GPU cluster networking, NCCL, RDMA, or RoCEv2.
Familiarity with networking technologies including SONiC, SAI, BGP, EVPN, VXLAN, DPDK, and Kubernetes.
Experience in Software Quality Assurance with Ethernet ASICs in Layer2, Layer3, and Buffer management, Scheduling and Queueing
Experience with network validation tools such as Ixia, Keysight, Spirent, or similar traffic generation and test platforms.
Experience with distributed systems, cloud infrastructure, deployment automation, or infrastructure-as-code.
Exposure to customer deployments, field engineering, or forward-deployed engineering is a plus.