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More fit checks, tailored CVs, and complete prep for every role.
Job Description
Requirements
Hardware Bring-Up & Embedded SW: Drive custom firmware, Linux kernel driver design, memory management optimization, and hardware-software co-design for early-stage ASIC, CPU, and GPU architectures.
AI Compilation & Inference Runtimes: Build high-performance AI compiler paths and runtime engines (e.g., Triton, TVM, MLIR, OpenXLA) capable of executing complex model topologies with peak hardware utilization.
Data Center Scale & Orchestration: Design the software abstraction and virtualization layers needed to seamlessly cluster, schedule, and orchestrate our silicon across distributed data center infrastructure (e.g., Kubernetes, Ray, Slurm).
Early-Stage Team Building: Recruit, mentor, and hands-on manage a highly agile, world-class engineering team spanning embedded systems, compiler design, and infrastructure engineering.
Hardware-Software Co-Design: Collaborate intensely with silicon architects and hardware engineering teams to evaluate workload bottlenecks, running real-world model benchmarks to directly influence future chip revisions.
Agile Product Delivery: Establish lean, rapid software development lifecycles. Shift quickly from early proof-of-concept and chip bring-up to alpha/beta releases for early data center customers.
Customer-Centric Ecosystem Integration: Ensure our software stack exposes clean, frictionless APIs and frameworks so enterprise data center clients can seamlessly swap their workloads onto our hardware.
Proven Executive Leadership: 10+ years of software engineering experience, with a track record as a VP of Engineering, VP of R&D, or Head of Software leading high-performing, multi-layered engineering organizations.
Startup DNA: Proven experience navigating the fast-paced, high-ambiguity landscape of early-stage startups or hardware bring-up phases. Comfortable being both a strategic architect and a hands-on technical driver.
Data Center & Compute Expertise: Deep technical understanding of data center compute workloads, distributed system scalability, and the infrastructure requirements for high-density AI inference deployment.
Cross-Domain Competency: A career that effectively blends silicon-adjacent development with high-level software infrastructure:
Solid grounding in low-level environments, kernel-level drivers, memory layout, and semiconductor ecosystem dynamics (such as ARM or custom compute blocks).
Direct experience building or managing teams that deal with deep learning compilation frameworks, model optimization (quantization, pruning), and high-throughput execution engines.
Strategic & Pragmatic Execution: Exceptional capability to translate early-stage business milestones into crisp software roadmaps, focusing engineering resources on time-to-market and performance benchmarks rather than over-engineering.