Top Tech Jobs & Startup Jobs

Develop agentic workflows and SDK capabilities for bringing up, testing, and optimizing generative AI models on custom accelerator silicon. Focus on LLM inference, numerical optimization, quantization, precision, emulation, model accuracy, latency, throughput, memory efficiency, and distributed execution. Collaborate with model, compiler, runtime, hardware, and architecture teams to deliver reliable software and inform future chip designs.

Develop system-level platform software for high-performance AI inference systems, including firmware, bootloaders, BSPs, device drivers, Linux kernel modules, debugging tools, and tests. Support hardware and silicon bring-up, validation, performance and power optimization, and hardware/software co-design. The role requires close collaboration across software, hardware, silicon, and validation teams, with occasional travel to partners, customers, or manufacturing sites.

Develop system-level software, firmware, tools, and tests for high-performance AI inference platforms. Responsibilities include Linux kernel and driver development, bootloader and BSP work, hardware and silicon bring-up, debugging, profiling, performance and power optimization, and low-level interface integration. The engineer will collaborate with hardware, validation, and silicon teams to deliver production-quality software and support platform deployment in data centers.

Design and develop distributed cloud services in Rust, maintain in-house Rust software, build generative AI APIs, and collaborate across teams on technical specifications and implementation. The role also involves coordinating agent teams and applying asynchronous Rust design patterns, networking concepts, HTTP, Kubernetes, and related cloud technologies.

Maintain and enhance Tensordyne’s internal developer platform, including CI pipelines, cloud development and build environments, Rust-based developer services, ML workflow orchestration, and agentic platforms. Manage and optimize GCP infrastructure while supporting scalable developer workflows and MLOps capabilities. The role involves infrastructure as code, Kubernetes, Git, Python, Flyte, and modern software engineering practices.

Build and optimize a backend compiler that transforms AI workloads into microcode for custom inference silicon. Responsibilities include lowering, instruction scheduling, register and resource allocation, memory hierarchy optimization, tiling, fusion, software pipelining, cost modeling, dynamic-shape support, and compiler interfaces for engineers and AI agents. The role also involves co-designing compiler, runtime, kernels, ISA, and silicon while improving performance against real hardware.

Lead ASIC front-end physical design and workflow development for generative AI inference chips. Optimize block-level and full-chip implementation using Cadence and Synopsys tools, including synthesis, formal checks, power analysis, and static timing analysis. Support backend design activities involving clock trees, routing, DRC, LVS, metal fill, and artwork release. Collaborate with global ASIC, design, and verification teams while developing scalable, high-performance EDA workflows.

Lead full-chip and block-level ASIC static timing analysis, timing closure, signoff, and workflow development for generative AI accelerator products. Develop hierarchical constraints and SDCs for high-speed interfaces and multi-clock domains, analyze clocking and routing issues, generate timing ECOs, and improve Cadence/Synopsys-based physical design flows. Collaborate with global ASIC design and verification teams as a hands-on technical leader.

Develop and maintain functional models for an AI inference accelerator. Implement and test architectural features, investigate discrepancies between models and hardware, validate models against architecture specifications and RTL, improve modeling techniques, and maintain the codebase while collaborating across architecture, hardware, and software teams.

Leads strategic technical engagements for an AI inference platform from architecture evaluation through benchmarking, model enablement, deployment, and production rollout. Builds and manages a high-performing solutions engineering team, guides customer evaluations and deployments, translates customer requirements into product priorities, and develops scalable technical go-to-market assets. Requires deep expertise in AI inference, accelerator architecture, distributed systems, serving frameworks, performance optimization, and customer-facing technical leadership.