The posting
About Company
Founded in 2022, XG Tech is driving the future of smart vehicles. Its mission is to empower the digital transformation of automobiles, moving from distributed computing to a centralized, cross-domain platform.
XG Tech focuses on the intelligent cockpit—the next frontier of differentiation—while seamlessly integrating advanced driving systems. By reimagining cars as mobile living spaces, XG Tech aligns with the evolving trend of vehicles becoming the “third living space.”
Role Summary As a Research Scientist (AutoResearch on LLM), you will focus on cutting edge AI-Driven AI development using Autoresearch(Automated MachineLearning) for Large Language Models (LLM). You will design and deploy automated research frameworks that autonomously discover. optimize, and train the next-generation LLM architectures. You will bridge the gap between hardware constraints and model design ensuring that the automatically optimized architectures achieve or exceed the pre-training efficiency and downstream performance of state of the art established base models
Key Responsibilities
- Design, build and scale automated search and optimization systems (Such as Neural Architecture Search, evolutionary algorithms, or LLM-driven research agents) to autonomously discover optimal LLM architectures
- Analyze and integrate hardware level constraints (e.g. tensor parallel limits, memory bandwidths, latency, cache hierarchies, FLOPs, and energy efficiency of target silicon) into the optimization loop
- Scale up discovered architectures to perform large-scale pre-training. Ensure the final models meet to exceed the performance, training efficiency, and convergence rate of existing top-tier foundation models
- Develop accurate, cost effective proxy task, evaluation protocols, and scaling laws to predict full-scale LLM performance from early-stage automated search limits
- Collaborate closely with chip architects, system/compiler engineers, and foundation model researchers to co-optimize hardware execution efficiency and model training algorithms
- Stay at the forefront of AutoML, hardware-software co-design, and LLM pre-training literature, contributing to peer-reviewed publications and IP generation where applicable
How will you stand out
- Master's or PhD degree in Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field with a focus on Deep Learning
- Strong research background in LLM pre-training, Transformer architectures, and scaling dynamics
- Hands-on experience with AutoResearch, AutoML, automated optimization algorithms, or using AI agents/LLMs for automated scientific discovery
- Solid understanding of GPU/accelerator architectures, memory hierarchies, parallel training strategies(tensor, pipeline, data parallel) and hardware performance profiling
- Production grade coding skills in Python and deep learning frameworks (e.g. Pytorch, JAX, Megatron-LM, Deepspeed).
- Demonstrated experience in training, scaling and evaluating large scaling models from scratch
- Having first author publications in top tier machine learning or system conferences (e.g. NeurIPS, ICML, ICLR, ASPLOS, ISCA, MLSys) is preferred
- Experience writing custom kernels (e.g. Triton, CUDA) or working with machine learning compilers are preferred
- Direct experience working with silicon/chip design teams to customize model architectures for specific ASIC/GPU/FPGA constraints.
- Experience building self improving AI Loops or automated coding research assistants
XG Tech is committed to providing equal employment opportunities by country, state, and local laws. XG Tech does not discriminate against employees or applicants based on conditions such as race, color, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.
