Sr. Principal Engineer, Scale-Up (Data Path Features)

Senior individual-contributor validation role. You own how data-path features are qualified on our scale-up AI networking platform, from the SAI/SDK programming path through ASIC forwarding and traffic-management behavior. You set the test strategy, own feature readiness and release sign-off, debug the hardest cross-stack failures, and grow the QA engineers around you. Primary depth is traffic management. Feature scope (cycle-accurate model and silicon), * Classification and marking: DSCP and 802.1p to traffic class, trust, TC-to-queue, remarking, port versus switch map binding, classification miss, ACL rewrite

• Queueing: unicast and multicast queues, counters, occupancy, watermarks, drops
• Scheduling: strict priority, WRR/DWRR, hierarchy, shaping, rate limiting, fairness under oversubscription
• Buffering: ingress and egress pools, static and dynamic thresholds, priority groups, headroom, reserved versus shared
• PFC and lossless Ethernet: pause, headroom, lossless queues, watchdog, storm and deadlock, DCB
• ECN and WRED: thresholds, drop probability, ECT/CE, interaction with DCQCN under RoCEv2
• Policing: color-blind and color-aware, CIR/PIR/CBS/PBS, trTCM
• Forwarding and counters: L2/L3, ECMP, mirror and sample, telemetry that makes the above observable
• Own the validation strategy, test plans, coverage, and release entry/exit criteria for the features above
• Qualify lossless and congestion behavior under RoCEv2 and GPU collectives (NCCL/RCCL) at line rate, including incast, PFC headroom, ECN accuracy, and no-drop
• Build the frameworks and debug tools the team depends on, including model-based checks of classification, queueing, scheduling, and drop or mark outcomes
• Lead root-cause analysis across software, SDK, model, silicon, and Linux, and decide when the spec, the implementation, or the test is wrong
• Mentor QA engineers, review their plans and automation, and represent validation in design reviews and the development-to-QA handoff
• Support customer deployments, proofs of concept, and field issues on data-path and congestion behavior

• 15+ years in networking software, system validation, or ASIC/platform QA, with a substantial stretch as a senior individual contributor
• Deep hands-on traffic management: PFC, scheduling, buffers, classification and marking, queueing, ECN/WRED
• Strong L2/L3 and ACL fundamentals, and experience with SAI, a switch SDK, or a NOS such as SONiC or FBOSS
• Ownership of validation frameworks and automation, not only test execution
• Python or Go at framework-review level; solid Linux and packet- and counter-level debugging
• Proven mentoring and influence as an individual contributor, and clear written readiness and root-cause reports

Preferred

• Ethernet ASIC QA in L2/L3, buffers, scheduling, and queueing
• RoCEv2, RDMA, DCB, PFC tuning, and DCQCN on AI or HPC fabrics; NCCL/RCCL on NVIDIA or AMD GPU clusters
• ASIC bring-up, C-model or emulation, and pre-silicon to post-silicon correlation
• Buffer and headroom sizing; incast, microburst, and oversubscription debug
• Ixia, Keysight, Spirent, or similar line-rate tools
• NVLink or ESUN; customer, field, or forward-deployed work is a plus

Where you fall within that range depends on your experience, skills, and impact-we benchmark against internal levels to keep things fair and consistent.

Job DescriptionWhy join Upscale, Inc.

Upscale, Inc., 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 is where you can produce meaningful work at the frontier-and operate at a high standard.