Jobs at HowURun

Founding ML Performance Engineer will write custom CUDA kernels, optimize end-to-end model inference for sub-50ms latency, drive 10x stack performance (memory bandwidth, kernel fusion, scheduling), implement zero-copy multi-GPU/multi-node memory, own GPU utilization and memory management, profile and benchmark pipelines, and build performance tooling and standards.

Open call for exceptional candidates across functions. Seeking technical leaders and engineers who have built large-scale, low-latency, GPU-heavy inference or serving infrastructure, or top performers in GTM, design, devrel, or ops. Must own ambiguous problems end-to-end, set direction with limited scaffolding, and maintain high standards. Early-stage equity, competitive salary, full benefits, and top-tier AI tooling provided.

Founding ML infrastructure engineer to design and scale a GPU compute platform (1,000+ GPU clusters), build multi-tenant scheduling and marketplace infrastructure, own production reliability, architect feature stores/model registries and experiment tracking, and develop resource orchestration and scheduling to optimize throughput, cost, and latency.

Design and own a cloud-native, low-latency platform for real-time, stateful AI inference and live video generation: manage GPU-heavy Kubernetes clusters, observability, autoscaling, security, and platform primitives while collaborating with ML teams and mentoring engineers.

Founding SRE responsible for defining reliability culture, SLOs, observability, incident response, on-call processes, capacity planning, and automation across a distributed GPU inference platform.

Design, build, and maintain low-latency, high-throughput backend services and APIs for a real-time, stateful AI inference runtime. Collaborate with infrastructure and applied AI teams to ensure reliability, performance, observability, and scalability of interactive, streaming systems. Influence architecture and translate experimental AI capabilities into production-grade software.