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Positron

Memory-first AI inference silicon for LLMs

Senior Physical Design Engineer - Synth

Silicon & ASIC DesignUpdated on Nov 14
Full-time

About the Job

Requirements

  • At least 8 years of ASIC implementation experience, including at least two successful tapeouts.
  • Strong hands-on proficiency in synthesis and flow development.
  • Proven experience closing timing on complex, multi-million-gate designs.
  • Deep working knowledge of Cadence Genus and/or Synopsys Design Compiler.
  • Strong Synopsys Design Constraints expertise, including constraint development and Multi-Mode Multi-Corner environments.
  • A solid grasp of RTL design principles and implementation-friendly coding practices.
  • Hands-on experience with advanced process nodes; 7nm and below is preferred.
  • Strong Tcl and Python scripting ability for automation and flow development.
  • Excellent debugging and root-cause analysis skills.

Responsibilities

  • Own synthesis for assigned partitions and subsystems, delivering clean, high-quality-of-results netlists on schedule.
  • Contribute to the development and improvement of synthesis flows, including constraint development, timing budgeting, quality-of-results optimization, incremental synthesis, and hierarchical implementation.
  • Help define and apply signoff criteria for synthesis deliverables.
  • Run hands-on synthesis using industry-leading electronic design automation tools, including Cadence Genus and Synopsys Design Compiler or equivalent.
  • Develop and maintain Synopsys Design Constraints, clock definitions, false paths, multicycle paths, generated clocks, and Multi-Mode Multi-Corner environments.
  • Track and continuously improve synthesis quality of results for assigned blocks.
  • Drive logical timing closure for assigned partitions, optimizing across frequency, area, power, congestion, and wireability.
  • Perform root-cause analysis of timing bottlenecks and contribute to chip-level timing budgets.
  • Work with architecture and RTL teams to identify and resolve timing risks before physical implementation.
  • Partner with backend implementation engineers on physical-aware synthesis, retiming, register balancing, pipeline optimization, and logic restructuring.
  • Give RTL teams actionable feedback so designs are implementation-friendly from the outset.
  • Collaborate with architecture, RTL, verification, physical design, design-for-test, intellectual property providers, and backend design services to drive rapid issue resolution.
  • Work with external implementation partners to review quality-of-results, timing, and area reports, and support constraint methodology and acceptance criteria.
  • Apply artificial intelligence tools to improve timing analysis, quality-of-results regression, constraint generation, timing debugging, report analysis, documentation, and engineering automation.

Desired Qualifications

  • Experience with AI accelerators, CPUs, or GPUs.
  • Experience at 5nm and below.
  • Familiarity with LPDDR, PCIe, or SerDes interfaces.
  • Exposure to chiplet-based architectures and multi-clock system-on-chip designs.
  • Experience mentoring junior engineers or leading synthesis for a subsystem.

About the Company

Positron

Memory-first AI inference silicon for LLMs

Positron develops memory-first AI inference silicon and systems to scale large-language-model serving. Its Atlas systems rely on commodity LPDDR5X memory rather than expensive HBM/CoWoS to boost bandwidth and capacity, enabling more efficient LLM deployment at hyperscale. The company’s next-generation Asimov silicon (tape-out planned for late 2026; production in 2027) pairs compute with very large memory per chip (roughly 288 GB to 2,304 GB) and is designed to be combined into Titan configurations (four to eight Asimov chips) for extremely large models and long context windows. Positron has already deployed Atlas systems in the cloud, including 50+ racks at Oracle Cloud Infrastructure and partnerships with Parasail, Jump Trading, and i3d.net. Its goal is to provide scalable, memory-rich AI inference hardware that lowers cost and increases throughput for serving large language models.

Company size
51-200
Company stage
Series C
Total funding
$1.2B
Headquarters
Reno, NV
Founded
2023

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