Modeling Architect

About NeurophosThe demand for new data centers and AI compute is rapidly outpacing the planet’s energy capacity. Digital solutions are hitting a power wall as we approach the physical limits of traditional silicon. Conquering this bottleneck means rethinking the fundamental architecture of inference compute. The industry’s current path can’t meet the need, so we’re taking a different approach.Instead of traditional electronic circuits, we use silicon photonics and an active, programmable metasurface to perform matrix multiplications at the speed of light. Our optical cells are 10,000x smaller than traditional photonic components, enabling unprecedented density. By using photonics instead of electricity, our chips become more efficient as they scale. This architecture will deliver up to 100 times the energy efficiency of existing solutions while significantly improving performance for large-scale AI inference.We’ve assembled a world-class team of industry veterans and recently raised a $110M Series A led by Gates Frontier. Participants include M12 (Microsoft’s Venture Fund), Carbon Direct Capital, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others.Join us and shape the future of computing!Location: Austin, TX or Sunnyvale, CA. Full-time onsite position.Reports To: Sr. Director of ModelingFLSA Status: ExemptPosition OverviewWe are seeking a modeling architect for hands-on architecture modeling of the T100 optical inference accelerator, with hardware/software co-design in the loop. You will work alongside senior engineers across two tracks. The first is analytical and system performance: roofline and limiter analyses, architecture performance models, workload setup, and the resulting plots and reports. The second is hardware models: functional, performance, and power models of compute blocks, memory, and hardware/software interfaces built in an event-driven simulator, plus RTL simulation.We expect real depth in one track and will help you build breadth across both. Most engineers start with a bounded piece, a single workload, a hardware block, or one layer of the model stack, and take on the surrounding area as the models mature.Key ResponsibilitiesBring up inference workloads as they ship, including dense and Mixture of Experts (MoE) transformers, hybrid/SSM models, prefill versus decode, KV cache, expert routing, and quantization, plus retrieval, speech, vision, and recommendation workloads where they map onto the accelerator.Bind Hugging Face and PyTorch workloads to the programming model and run them on the functional model.Co-design tiling, scheduling, the instruction set architecture (ISA), the SRAM and High Bandwidth Memory (HBM) hierarchy, and multi-chip mapping.Build in one or more layers of the modeling stack: roofline and limiter studies; Python energy and latency models; C++ functional models of optical GEMM, SRAM vector processors, dataflow engines, and HBM; cycle-approximate performance and power models; and RTL simulation with Verilator and SystemVerilog.Own the tests, configs, and plots behind a result so anyone can rerun it and see what was assumed.Use coding agents on real multi-file edits, and own the review of the C++ and SystemVerilog they generate.Share results with the architects setting the design, as well as the compiler, runtime, and RTL teams, and carry their questions back into the model stack.QualificationsBS or MS in Computer Engineering, Electrical Engineering, Computer Science, or a related field.3+ years of experience in hardware modeling, performance simulation, computer architecture, or related work.Proficiency in Python or modern C++ (C++17 or later). Python-first and C++-first backgrounds are both welcome.Working knowledge of computer architecture and microarchitecture, including pipelines, caches, memory hierarchies, and instruction set architecture (ISA).Ability to turn an LLM, GEMM, or accelerator paper into a workload config using Hugging Face or PyTorch.Strong debugging skills and the habit of writing down what was run, what was assumed, and what the number means.Preferred SkillsMS or PhD in Computer Engineering, Electrical Engineering, or Computer Science.Experience with roofline analysis, limiter analysis, GPU benchmarking, or correlating a model against published numbers.Event-driven, cycle-approximate, or cycle-accurate simulation with SystemC, gem5, SST, or a custom kernel.SystemVerilog, Verilog, Verilator, or RTL co-simulation.Memory and interconnect experience with HBM, DRAM, cache, SRAM, network-on-chip (NoC), AXI, or DMA.Familiarity with CUDA, GPU programming, or PyTorch internals.What We OfferThis is an opportunity to play a pivotal role in an innovative startup redefining the future of AI hardware. Work on game-changing technology at the intersection of photonics and AI as part of a collaborative, brilliant team. You’ll contribute to a platform that redefines computational performance and accelerates the future of artificial intelligence. Come help us bring this transformative technology to the world.BenefitsJoin a team that invests in your future and your well-being. At Neurophos, we offer:100% coverage of base health plan premiums for you and your dependents, plus HSA contributions.Unlimited PTO. No rigid vacation banks, just a focus on delivery.401(k) matching and stock option opportunities to ensure our success is your success.Full suite of voluntary benefits, including Dental, Vision, Life, Hospital, Critical Illness, and Accident insurance.Personalized Benefits. Choose the plans that fit your life and take the cash back for those that don’t.

• BS or MS in Computer Engineering, Electrical Engineering, Computer Science, or a related field.
• 3+ years of experience in hardware modeling, performance simulation, computer architecture, or related work.
• Proficiency in Python or modern C++ (C++17 or later). Python-first and C++-first backgrounds are both welcome.
• Working knowledge of computer architecture and microarchitecture, including pipelines, caches, memory hierarchies, and instruction set architecture (ISA).
• Ability to turn an LLM, GEMM, or accelerator paper into a workload config using Hugging Face or PyTorch.
• Strong debugging skills and the habit of writing down what was run, what was assumed, and what the number means.

Preferred Skills

• MS or PhD in Computer Engineering, Electrical Engineering, or Computer Science.
• Experience with roofline analysis, limiter analysis, GPU benchmarking, or correlating a model against published numbers.
• Event-driven, cycle-approximate, or cycle-accurate simulation with SystemC, gem5, SST, or a custom kernel.
• SystemVerilog, Verilog, Verilator, or RTL co-simulation.
• Memory and interconnect experience with HBM, DRAM, cache, SRAM, network-on-chip (NoC), AXI, or DMA.
• Familiarity with CUDA, GPU programming, or PyTorch internals.

Join a team that invests in your future and your well-being. At Neurophos, we offer:

• 100% coverage of base health plan premiums for you and your dependents, plus HSA contributions.
• Unlimited PTO. No rigid vacation banks, just a focus on delivery.
• 401(k) matching and stock option opportunities to ensure our success is your success.
• Full suite of voluntary benefits, including Dental, Vision, Life, Hospital, Critical Illness, and Accident insurance.
• Personalized Benefits. Choose the plans that fit your life and take the cash back for those that don’t.

The demand for new data centers and AI compute is rapidly outpacing the planet’s energy capacity. Digital solutions are hitting a power wall as we approach the physical limits of traditional silicon. Conquering this bottleneck means rethinking the fundamental architecture of inference compute. The industry’s current path can’t meet the need, so we’re taking a different approach.

Instead of traditional electronic circuits, we use silicon photonics and an active, programmable metasurface to perform matrix multiplications at the speed of light. Our optical cells are 10,000x smaller than traditional photonic components, enabling unprecedented density. By using photonics instead of electricity, our chips become more efficient as they scale. This architecture will deliver up to 100 times the energy efficiency of existing solutions while significantly improving performance for large-scale AI inference.

We’ve assembled a world-class team of industry veterans and recently raised a $110M Series A led by Gates Frontier. Participants include M12 (Microsoft’s Venture Fund), Carbon Direct Capital, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others.

Join us and shape the future of computing!