Top Tech Jobs & Startup Jobs

Lead definition and implementation of the SoC management subsystem for an AI accelerator: integrate ARM cores, PCIe, DDR/LPDDR, peripherals, debug/trace infrastructure, and design control/interrupt/driver interfaces while coordinating RTL, firmware, verification, and board teams to meet performance, power, and area targets.

Design and optimize high-performance compute kernels for AI primitives (GEMM, attention, convolution), profile and tune across heterogeneous hardware, collaborate with compiler/runtime teams (Triton, PyTorch, SYCL), prototype precision formats, and contribute micro-architecture feedback while writing reusable C++/CUDA/Triton/MLIR code.

Provide high-level executive support to founders (calendar, travel, expenses, correspondence) while managing office operations, vendor relationships, facilities, HR logistics, budget oversight, event planning, and new-hire onboarding for a 25–50 person startup.

Verify chip-level and performance behavior of custom ASICs: design end-to-end test flows with architecture team, run simulation-based verification, implement UVM environments, use constrained-random and functional-coverage methodologies, and coordinate vendors/contractors.

Develop and maintain system-level performance simulators and models for AI/data-intensive workloads; characterize real workloads (LLMs, GNNs, graph analytics); model memory, interconnect, and scheduling behavior; evaluate architectural trade-offs and produce quantitative comparisons; collaborate with hardware architects, compiler/runtime teams, partners, and stakeholders; translate performance modeling into clear narratives for technical and executive audiences.

Design, implement, and optimize compiler components for AI workloads using PyTorch, Triton, LLVM, and MLIR. Build IRs, codegen passes, and performance-tuned kernels for CPUs, GPUs, and custom accelerators. Contribute upstream to open-source projects and collaborate cross-functionally to enable scalable, high-performance deployment across diverse hardware.

Design, develop, and maintain core components of an AI-driven software stack enabling efficient communication with hardware. Implement API layers, command submission pipelines, and memory management; collaborate with hardware architects and kernel developers; debug across user-to-hardware stack and optimize for performance and throughput.

Build and operate developer infrastructure: CI/CD pipelines, dev environments, containerized builds, internal deployments, observability, and production-representative dev systems to accelerate engineers across compiler, runtime, kernel, and hardware teams.

Design, implement, and optimize compiler components for AI models targeting CPUs, GPUs, and custom accelerators. Enhance PyTorch Inductor/Dynamo, Triton, LLVM, and MLIR toolchains; create IRs, codegen passes, and performance optimizations; profile and tune kernels; and contribute to open-source projects.