We’re supporting an advanced-compute company building a new hardware and software platform for AI workloads.
They’re looking for an inference specialist who can optimize the complete path from transformer models through execution engines, compilers, runtimes and kernels to multi-accelerator systems.
What you’ll work on
- Architect high-performance transformer inference on a new compute platform
- Optimize model execution, graph transformations and runtime behavior
- Develop or guide performance-critical C++, CUDA or Triton components
- Improve attention, GEMM, MoE and other critical execution paths
- Design KV-cache, batching, memory-management and decoding strategies
- Optimize tensor, pipeline and expert parallelism
- Analyze multi-device and multi-node inference performance
- Drive improvements in latency, throughput, utilization and cost per token
What we’re looking for
- Principal, Distinguished or equivalent senior technical scope
- Direct optimization of transformer or generative-AI inference
- Hands-on low-level implementation in C++, CUDA, Triton or similar
- Deep expertise across multiple connected layers of the inference stack
- Strong profiling and performance-debugging skills
- Experience taking optimizations beyond isolated kernels into complete systems
Principal Inference Engineer in san francisco at Unknown Company
This position is listed as full time and onsite.