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Velaura

Designs energy-efficient AI silicon and systems

RTL Engineer Memory Subsystem

Silicon & ASIC DesignConfirmed live in the last 24 hours
Full-timeSanta Clara, CA, USA$150k - $250k /yr

About the Job

Requirements

  • Experience designing RTL for complex digital systems, with hands-on ownership of memory subsystem blocks.
  • Demonstrated experience integrating a memory controller with a memory PHY, including controller-to-PHY interfaces such as DFI, initialization and training sequences, and bring-up.
  • Strong understanding of high-speed, low-power memory technologies such as LPDDR4, LPDDR4X, LPDDR5, LPDDR5X, DDR, and HBM, including their protocol, timing, and power-state behavior.
  • Strong understanding of computer architecture, microarchitecture, and digital design fundamentals.
  • Expert-level Verilog and SystemVerilog skills and experience with modern RTL design methodologies, including lint, clock-domain crossing analysis, synthesis-aware coding, and low-power intent.
  • Familiarity with performance, power, and area tradeoffs in the memory path.
  • Strong debugging and problem-solving skills.
  • Ability to work effectively in a collaborative, multidisciplinary engineering environment.

Responsibilities

  • Design, implement, and optimize RTL for the memory subsystem of the Velaura SoC, including memory controllers, fabric interfaces, and the logic that integrates with external memory PHYs.
  • Own memory controller and PHY integration, including controller-to-PHY interfaces such as DFI, initialization and training flows, calibration, and bring-up hooks.
  • Drive high-speed, low-power design of the memory path, including timing closure at high data rates, power-state management such as self-refresh and power-down modes, clock and power gating, and DVFS interactions.
  • Work with software teams to define hardware interfaces, memory maps, execution flows, and performance-critical interactions such as scheduling, prefetching, and quality of service.
  • Analyze bandwidth, latency, and utilization bottlenecks in the memory path and propose architectural and implementation improvements.
  • Optimize designs for performance, power, area, scalability, and reliability, including RAS features such as ECC and error reporting in the memory path.
  • Partner closely with verification and physical design teams throughout the development cycle, and with memory IP and PHY vendors during integration and silicon bring-up.
  • Leverage modern engineering tools, including AI-assisted development workflows, to improve productivity, quality, and design exploration.
  • Participate in design reviews and contribute to a culture of technical excellence.

Desired Qualifications

  • Familiarity with standard interconnect protocols such as AXI, CHI, and ACE on the controller's system-side interface, including quality-of-service mechanisms.
  • Experience with memory controller and PHY bring-up on silicon, including training and calibration debug and margining.
  • Knowledge of reliability, availability, and serviceability in the memory path, including inline and side-band ECC, scrubbing, poisoning, and error reporting.
  • Experience with low-power design techniques and power management architectures across memory and clock and power domains.
  • Experience developing a memory controller from the ground up, including command scheduling and arbitration, bank and rank management, refresh management, and reordering.
  • Knowledge of functional safety standards such as ISO 26262 and ASIL as they apply to memory-path design.
  • Experience with AI, machine learning, or edge AI hardware, especially the memory access patterns of tensor workloads.
  • Familiarity with robotics, drones, autonomous vehicles, or industrial automation systems.
  • Exposure to performance modeling, emulation, FPGA prototyping, or silicon bring-up.
  • Experience using modern AI tools and workflows to accelerate engineering productivity.

About the Company

Velaura

Designs energy-efficient AI silicon and systems

Velaura AI designs and manufactures ultra-energy-efficient AI infrastructure, including 3nm ASIC chips, Teraflux mining systems, FluxVision fleet-management software, and AuraLinks rack-level networking. Customers deploy the hardware in data centers, while FluxVision monitors and manages fleets; EnergyTune and AutoTune dynamically adjust power to meet grid stability, and AuraLinks reduces bottlenecks in AI training and inference. The company differentiates itself with a team of silicon architects from Apple, Google, and Qualcomm, plus a patent-pending energy-management approach and a fully integrated hardware-software stack. Its goal is to enable massive AI deployment by delivering powerful, energy-efficient compute and networking that aligns with sustainable energy grids and domestically produced technology.

Company size
51-200
Company stage
Series A
Total funding
$424M
Headquarters
Santa Clara, CA
Founded
2022

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