Design and implement the LLM inference runtime for frontier models running on custom silicon.

Build scheduling, continuous batching, memory management, KV-cache management, and execution orchestration for high-performance inference.

Develop distributed execution strategies across chips, hosts, and racks, including model partitioning, communication, and synchronization.

Optimize end-to-end latency, throughput, memory efficiency, and hardware utilization across diverse model architectures and serving workloads.

Partner with kernel, compiler, architecture, and silicon teams to co-design interfaces and remove performance bottlenecks across the stack.

Enable new model features, execution patterns, numerical formats, and hardware capabilities in a reliable production runtime.

Create profiling, observability, benchmarking, and performance-modeling tools that make runtime behavior measurable and actionable.

Debug complex correctness, performance, and reliability issues spanning model code, runtime software, communication layers, and hardware.

Turn workload insights into clear requirements for future generations of silicon and system architecture.

You might thrive in this role if:

Have strong systems programming experience in C++, Rust, Python, or comparable performance-oriented environments.

Have built or optimized runtimes, distributed systems, compilers, kernels, model-serving infrastructure, or adjacent systems software.

Software Engineer, Model Runtime

Responsibilities