Principal Architect, Memory-Centric Computing · AI Infrastructure

You connect AI workload behavior, memory hierarchy (including DRAM and Flash tiers), connectivity/fabric, and storage/IO into coherent, quantified arguments — evaluating a broad solution space rather than advocating for any single technology.

12+ years in system architecture, performance engineering, or infrastructure modeling with a track record of studies that influenced product direction, investment decisions, or platform strategy.

AI infrastructure fluency.

Working knowledge of training and inference bottlenecks, data movement patterns, and memory pressure across transformers, MoE, and recommendation workloads.

Memory and storage grounding.

Solid understanding of memory hierarchy and tiering principles across DRAM and Flash; storage/IO fundamentals including tail latency, QoS, and NVMe/NVMe-oF behavior; and connectivity/fabric options for shared, pooled, and disaggregated memory.

The pay range below is for all roles at this level across all US locations and functions. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. We also offer incentive opportunities that reward employees based on individual and company performance.

This is in addition to our diverse package of benefits centered around the wellbeing of our employees and their loved ones. In addition to the usual Medical/Dental/Vision/401k, our inclusive rewards plan empowers our people to care for their whole selves. An investment in your future is an investment in ours.

Give Back

With a charitable giving match and frequent opportunities to get involved, we take an active role in supporting the community.

Enjoy Time Away

You’ll start with 4+ weeks of paid time off a year, plus holidays and sick leave, to rest and recharge.

Architecture Strategy & Trade Studies

Define and evaluate the memory solution space — GPU-side shared memory, DRAM and Flash capacity tiers, pooled/disaggregated memory, and fabric-attached approaches — with quantified value propositions across performance, power, cost/TCO, density, and operability

Identify break-even conditions and decision criteria across solution approaches; produce architecture briefs and sensitivity analyses ready for executive audiences

Workload-Driven Analysis

Ground every architectural comparison in real AI behavior: large model training/inference (including long-context and KV-cache dynamics), MoE and sparse workloads, multi-step agentic pipelines, and recommendation/embedding workloads

Build and maintain a workload methodology — microbenchmarks, proxy models, traces — tied to throughput, latency, tail latency, utilization, and SLA impact