Jobs at Neurophos

Coordinates technical interview scheduling, onsite candidate experiences, travel logistics, debriefs, interviewer feedback, and ATS data integrity. Builds recruiting playbooks, templates, workflow standards, and interviewer training while improving hiring processes and candidate communication. Supports recruiting leaders and technical recruiters in a fast-growing startup environment, including managing overlapping priorities and confidential candidate information.

Own People Operations infrastructure for a rapidly scaling startup, including onboarding, multi-state employment compliance, HRIS administration, benefits, leave programs, policies, performance cycles, employee relations, offboarding, and international employment support. Partner with Finance, Legal, Recruiting, Facilities, and EHS on payroll, equity, workers’ compensation, and compliance matters. This is the company’s first dedicated People Operations hire and requires full-time onsite work in Austin.

Own the design-for-test architecture and strategy for complex ASICs, including ATPG scan, MBIST, boundary scan, JTAG, HBM, analog test, and wafer/package-level testing. Lead coverage assessment, signoff, test vector development, RTL and physical-design testability, production test-plan delivery, and post-silicon handoff. Establish the company’s DFT flow, metrics, and processes while collaborating with analog, operations, package, physical-design, and OSAT teams.

Own the end-to-end performance model for an optical vector-matrix multiplication engine. Build optical and electrical link budgets, behavioral signal-chain models, and accuracy, throughput, latency, and power allocations. Model noise, distortion, jitter, quantization, crosstalk, mismatch, and nonlinearities using Monte Carlo, corner, and time-domain simulations. Correlate models with laboratory and silicon measurements, lead trade studies, collaborate across engineering teams, mentor engineers, and contribute to patents.

Own calibration architecture, algorithms, system integration, laboratory bring-up, and verification for an optical vector-matrix multiplication engine. Develop control and optimization methods for optical phase, amplitude, and timing calibration; specify calibration hardware and software interfaces; lead hardware characterization and debug; correlate measurements with models; define diagnostics and verification plans; and mentor engineers. The role requires extensive hands-on work with electro-optic or mixed-signal systems, test equipment, automated measurement software, and cross-functional design teams.

Leads design verification methodology and team development for complex compute ASICs. Owns UVM testbench architecture, multi-level verification planning, VIP integration, gate-level simulation, FPGA-based verification, cloud-hosted emulation, formal verification, coverage closure, and AI-assisted testbench development. Partners across architecture, RTL, physical design, DFT, and validation to drive tapeout readiness.

Lead the end-to-end electronic IC program from architecture and specification through ASIC design, verification, tapeout, post-silicon bring-up, and production readiness. Coordinate internal engineering teams and external foundry, IP, OSAT, and design-service partners. Own schedules, dependencies, risks, readiness reviews, tapeout logistics, lab bring-up, silicon debug, and executive reporting. Establish AI-augmented program processes and drive cross-functional execution for a first-silicon mixed-signal ASIC supporting optical AI compute.

Model the architecture and performance of an optical AI inference accelerator through hardware/software co-design. Responsibilities include bringing up transformer and other AI workloads, integrating Hugging Face and PyTorch models, developing Python and C++ functional, performance, energy, and power models, conducting roofline and limiter analyses, modeling memory and compute blocks, simulating RTL, and maintaining reproducible tests, configurations, plots, and reports.

Build functional, performance, energy, power, and area models for AI accelerator workloads and hardware/software co-design. Bring up PyTorch and Hugging Face workloads, model optical GEMM, memory systems, tiling, scheduling, ISA, NoC traffic, and multi-chip mapping. Develop Python analytical models and bit-accurate C++ simulations, contribute to event-driven simulation infrastructure, correlate models with RTL through Verilator and SystemVerilog, establish modeling methodology, and mentor engineers.

Own performance and energy benchmarking for an optical AI inference accelerator. Build reproducible benchmarks across analytical models, architecture models, RTL simulation, and competitor GPUs. Bring up workloads from Hugging Face, PyTorch, papers, and inference stacks; measure latency, throughput, power, and energy; analyze bottlenecks; operate cloud or lab environments; and document configurations, logs, assumptions, and discrepancies.

Lead integration of full-chip or major SoC subsystems, owning top-level flows and cross-domain decisions across DFT, power, timing, and interconnect architecture. Partner with architecture, RTL, physical design, verification, and DFT teams to resolve system tradeoffs. Support organizational growth through hiring and mentoring while remaining hands-on with advanced-node, high-bandwidth, low-power chip designs.

Lead end-to-end physical design and SoC implementation from synthesis through tapeout on advanced process nodes. Build and automate synthesis, timing closure, power, clock, physical verification, and IP/library management flows. Integrate analog and mixed-signal components, support new process technologies, and collaborate with RTL, DV, and analog teams. Build, hire, and manage a multi-site physical design organization delivering optimized GDSII implementations for complex SoCs.

Lead development of a compiler toolchain for a custom LLM accelerator: design IR/graph transformations and lowering passes, optimize compute and memory patterns, integrate with PyTorch/JAX/Triton, build test infrastructure, and ensure correctness and performance for hybrid optical-electronic hardware.

Develop hardware-aware post-training quantization and model adaptation methods for LLMs, diffusion models, and other machine learning applications targeting optical inference hardware. The role involves numerical and constrained optimization, controlled experiments, research-quality implementations, model deployment across PyTorch, Triton, and JAX, GEMM optimization, and collaboration with hardware and software teams to co-optimize AI architectures. The engineer will also publish research while protecting company intellectual property.

Lead end-to-end datacenter scale-out networking architecture for the T100 platform: host networking stack to EIC PCIe bridge, NIC integration, and DMA into HBM. Define RDMA/transport/offload (RoCEv2, UET), PCIe P2P/ACS policies, queue/completion semantics, vendor selection, collective communication modeling, DPU roadmap, standards representation, and mentor architecture and software engineers.

Lead development of inference servers and racks, owning electrical, mechanical, and systems engineering from boards and interconnects through rack-level power and high-speed channel closure. Build and lead the Systems team, drive integration/bring-up, EVT/DVT/PVT sign-off, system reliability (RAS), and manage schedule and budget for full-scale product delivery.