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Architect
Palo Alto, California, United States; Bangalore, India
Source: Architect careers · View original posting
From Architect's posting. “We” and “our” refer to the employer.
Architect is a frontier AI lab for custom silicon. We partner with frontier labs, clouds / neoclouds, physical AI companies, and advanced fabs to tape-out custom chips co-designed for next-generation AI workloads. Our goal is to compress end-to-end software to silicon timelines, and maximize intelligence per watt and per dollar for the world. We are a small exceptional team across silicon, systems, software and frontier AI.
Our team have led research teams at nearly every frontier AI lab, and at some of the most complex SoCs in the world.
Required Qualifications
Degree
: Bachelor’s, Master’s, or PhD in Electrical Engineering, Computer Engineering, or a closely related field.
Experience
: 5+ years (10+ preferred) in RTL design with at least one advanced-node tapeout experience involving memory subsystems (DDR/LPDDR/HBM controllers, cache hierarchies, or memory-intensive SoC subsystems).
Memory Interface Expertise
: Deep familiarity with JEDEC memory standards — DDR5/LPDDR5X command/address protocols, timing parameters, training sequences, and/or HBM2E/HBM3 pseudo-channel architecture, stack addressing, and interleaving schemes.
Memory Controller Design
: Hands-on experience designing or owning memory controller blocks including command schedulers, bank state machines, refresh engines (per-bank, fine-granularity), read/write turnaround optimization, and PHY interface timing (DFI or proprietary).
Memory Hierarchy Architecture
: Experience with multi-level cache design (tag/data arrays, replacement policies, coherence protocols), scratchpad controllers, or unified memory architectures with partitioning and QoS.
SystemVerilog
: Clear, synthesizable, lint-clean RTL with strong design habits — parameterization for multi-standard support (DDR5/HBM3), modularity for channel/pseudo-channel instantiation, and configurability for different capacity/bandwidth targets.
Block-Level Depth
: Hands-on experience with SRAM controllers and arbiters, bank conflict resolution, address hashing/interleaving, ECC encode/decode engines, and high-bandwidth data movement between on-chip and off-chip memory.
SoC Methodology
: Solid grasp of synthesis, timing constraints, clock domain crossings (PHY-to-controller domain, multi-frequency memory interfaces), reset strategies, AMBA protocols (AXI, ACE, CHI), and power management for memory subsystems.
Python
: Strong skills for design automation, performance modeling, regression infrastructure, and tooling.
PPA Ownership
: Experience taking a memory controller or cache subsystem from RTL through synthesis and working with PD teams on timing/area/power closure — particularly for high-frequency controller logic and wide data buses.
Bonus Qualifications
Experience with HBM integration: interposer-level considerations, PHY calibration, thermal management impacts on refresh.
Familiarity with CXL memory pooling, Type 3 device controllers, or disaggregated memory architectures.
Near-memory or processing-in-memory (PIM) design experience.
Low-power design techniques: DVFS-aware memory scheduling, partial-array self-refresh, clock gating of idle channels, power gating of unused banks.
FPGA prototyping experience (Xilinx Vivado/Vitis) with DDR MIG or HBM subsystem IP integration.
SVA assertions for JEDEC protocol compliance (command sequencing, timing parameter checking, training state machines).
Prior IP building and delivery experience for DDR/LPDDR controllers, HBM controllers, or cache subsystem IPs.
Performance modeling: experience building or using cycle-accurate memory system simulators (e.g., DRAMSim, Ramulator) to validate microarchitectural decisions.
Domain-specific research contributions: publications or patents in memory systems, memory scheduling algorithms, or memory-centric compute architectures for ML workloads.
Why Architect
You’ll join a founding team building the future of chip design at the intersection of AI and silicon. Your memory subsystem expertise will directly shape production ASICs — enabling the bandwidth and efficiency that ML workloads demand — and influence how AI transforms hardware development from spec to tapeout.
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