Develop and evolve a state-of-the-art inference framework in modern C++ that extends TensorRT with autoregressive model serving capabilities, including speculative decoding, LoRA, MoE, and KV cache management.
Design and implement compiler and runtime optimizations tailored for transformer-based models running on constrained, real-time platforms.
Collaborate with teams across CUDA, kernel libraries, compilers, and robotics to deliver high-performance, production-ready solutions.
Contribute to CUDA kernel and operator development for critical transformer components such as attention, GEMM, and MoE.
Benchmark, profile, and optimize inference performance across diverse embedded and automotive environments.
Stay ahead of the rapidly evolving LLM/VLM ecosystem and bring emerging techniques into product-grade software.

Requirements

BS, MS, PhD, or equivalent experience in Computer Science, Electrical/Computer Engineering, or a closely related field.
4+ years of relevant software development experience.
Deep understanding of transformer models and inference optimization techniques (e.g., quantization, tensor parallelism, or memory-efficient scheduling).
Proficient programming ability with modern C++ (C++11/14/17 and beyond).
Familiarity with popular LLM frameworks and libraries such as TensorRT, TensorRT-LLM, vLLM, SGLang, MLC-LLM, or FlashInfer.
A track record of strong software design, execution, and collaboration across fields.

Benefits

eligible for equity and benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

C++TensorRTtransformer modelsinference optimizationquantizationtensor parallelismmemory-efficient schedulingcompiler optimizationsruntime optimizationsbenchmarking

Soft Skills

collaborationsoftware designexecution

Certifications

BS in Computer ScienceMS in Computer SciencePhD in Computer ScienceBS in Electrical/Computer EngineeringMS in Electrical/Computer EngineeringPhD in Electrical/Computer Engineering