Research Scientist (AutoResearch on LLM)

Our client is a Singapore-based automotive tech company, building AI-powered computing platforms for software-defined vehicles. They specialize in intelligent cockpit systems and autonomous driving computation, combining custom automotive SoCs with advanced software to help automakers deliver safer, smarter, more personalized in-car experiences.

Research Scientist

Role

Overview

As a Research Scientist (AutoResearch on LLM), you will focus on cutting edge AI-Driven AI development using Autoresearch(Automated Machine Learning) 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

Qualifications &

Requirements

· 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

EA Personnel Registration No: R1106329

EA License No: 12C6254