Tech Lead, Functional Modeling

Neurophos • Austin, TX

19d • Onsite

About The Position

At Neurophos, listed as one of EE Times’ 2025 100 Most Promising Start-ups, we are revolutionizing AI computation with the world’s first metamaterial-based optical computing platform. Our design addresses the traditional shortcoming of silicon photonics for inference and provides an unprecedented AI engine with substantially higher throughput and efficiency than any existing solution.
We've created an optical metasurface with 10,000x the density of traditional silicon photonics modulators. This enables a solution with 100x gains in power efficiency for neural network computing without sacrificing throughput; we've made improvements there, too. By integrating metamaterials with conventional optoelectronics, our compute-in-memory optical system surpasses existing solutions by a wide margin and enables truly high-performance and cost-effective AI compute.
Join us to shape the future of optical computing.
Location: San Francisco Bay Area or Austin, TX. Full-time onsite position.
Position Overview:
We are seeking an experienced Lead for Functional Modeling (FMOD) to establish and drive our functional modeling infrastructure, enabling early software development and RTL validation. This leadership role combines deep technical expertise in hardware functional modeling with team leadership responsibilities. You will build the FMOD team, define modeling methodologies using our YinYang event-driven framework, and create high-fidelity functional models of our novel optical computing platform that enable software teams to develop ahead of silicon.

Requirements

• MS or PhD in Computer Engineering, Electrical Engineering, or Computer Science (or BS with equivalent experience)
• 10+ years of experience in functional modeling, hardware emulation, or system-level simulation
• Proven experience building and leading technical teams (3+ years of management experience)
• Deep expertise in both execution-driven and trace-driven simulation methodologies
• Strong proficiency with SystemC and Transaction-Level Modeling (TLM 2.x)
• Expert-level C++ programming (C++17/20/23) with focus on modularity and performance
• Experience designing clean abstraction layers for complex hardware systems
• Track record of shipping functional models that enabled software development or RTL validation
• Excellent communication skills and ability to collaborate across hardware and software teams
• Understanding of computer architecture and accelerator design principles

Nice To Haves

• Experience with GPU architectures and CUDA programming
• Background in accelerator functional modeling (TPU, NPU, DSP, or similar)
• Familiarity with Verilator, SystemVerilog, or RTL co-simulation (DPI interfaces)
• Knowledge of memory system modeling (HBM, DRAM, cache hierarchies)
• Experience with event-driven simulation frameworks (gem5, SST, or custom frameworks)
• Understanding of ML workloads and framework internals (PyTorch, TensorFlow)
• Background in optical computing, photonics, or analog computing paradigms
• Experience with high-performance simulation optimization techniques
• Python expertise for scripting, analysis, and test infrastructure
• Publication record in hardware modeling or computer architecture

Responsibilities

• Lead the FMOD team (4+ engineers) focused on functional modeling and software enablement
• Architect and implement functional models of optical GEMM engines, SRAM vector processors, and dataflow engines
• Define functional modeling methodologies within the YinYang (libyy) event-driven framework
• Build transaction-level models (TLM) with clean interfaces between compute blocks
• Develop both execution-driven and trace-driven simulation capabilities
• Integrate SystemC/TLM 2.x models with custom C++ simulation infrastructure
• Enable early software development by providing high-performance functional simulators
• Collaborate with RTL teams on functional validation and co-simulation strategies
• Define modeling abstractions and component interfaces that enable team parallelism
• Mentor modeling engineers and establish team development practices
• Drive functional correctness validation through a comprehensive test infrastructure

Benefits

• A pivotal role in an innovative startup redefining the future of AI hardware.
• A collaborative and intellectually stimulating work environment.
• Competitive compensation, including salary and equity options.
• Opportunities for career growth and future team leadership.
• Access to cutting-edge technology and state-of-the-art facilities.
• Opportunity to publish research and contribute to the field of efficient AI inference.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree