Design, build, and operate the Model APIs surface with focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling and multi-modal serving
Profile and optimize TensorRT-LLM kernels, analyze CUDA kernel performance, implement custom CUDA operators, tune memory allocation patterns for maximum throughput and optimize communication patterns across multi-GPU setups
Productionize performance improvements across runtimes with deep understanding of their internals: speculative decoding implementations, guided generation for structured outputs, custom scheduling and routing algorithms for high-performance serving
Build comprehensive benchmarking frameworks that measure real-world performance across different model architectures, batch sizes, sequence lengths, and hardware configurations
Productionize performance improvements across runtimes (e.g.TensorRT, TensorRT‑LLM): speculative decoding, quantization, batching, and KV‑cache reuse
Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks to measure speed, reliability, and quality
Implement platform fundamentals: API versioning, validation, usage metering, quotas, and authentication
Collaborate closely with other teams to deliver robust, developer‑friendly model serving experiences