Research Scientist - LLM Training System Service - Global Frontier Tech Progra

Responsibilities

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by the end of year 2027. Please state your availability and graduation date clearly in your resume.

AML‑Ark combines system engineering and the art of machine learning to develop and maintain massively distributed ML training and inference systems around the world, providing high‑performance, highly reliable, and scalable systems for LLM/AIGC/AGI.

With the evolution from large language models (LLMs) to AI Agents, the training paradigm is undergoing a fundamental shift. Traditional distributed training frameworks like Megatron‑LM are designed around relatively static parallelism strategies, whereas Agent training introduces more dynamic patterns, including external tool interactions, multi‑step reasoning, and iterative self‑improvement. In this context, tightly coupled system design can limit flexibility and efficiency. We aim to build a robust architecture that cleanly separates “logical control” from “compute execution,” enabling more scalable and adaptable training workflows.

• Develop and optimize LLM training & inference & reinforcement learning frameworks.
• Work closely with model researchers to scale LLM training & reinforcement learning to the next level.
• Optimize GPU and CUDA performance to create an industry‑leading high‑performance LLM training and inference and RL engine.

• Currently pursuing a Ph.D. in computer science, automation, electronics engineering or a related technical discipline.
• Proficient in algorithms and data structures; familiar with Python.
• Understand the basic principles of deep learning algorithms, be familiar with the basic architecture of neural networks and understand deep learning training frameworks such as PyTorch.

• Proficient in GPU high‑performance computing optimization technology on CUDA; in‑depth understanding of computer architecture; familiar with parallel computing optimization, memory access optimization, low‑bit computing, etc.
• Familiar with FSDP, Deepspeed, JAX SPMD, Megatron‑LM, Ver Sl, TensorRT‑LLM, ORCA, VLLM, SGLang, etc.
• Knowledge of LLM models; experience in accelerating LLM model optimization is preferred.

Base salary range for this position in the selected city is $212,800 - $450,000 annually. Compensation may vary outside of this range depending on a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the total package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day‑one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short‑term and long‑term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of paid personal time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
• Exercising sound judgment.