Machine Learning Research Engineer

Description

Change the world. Love your job.

We are seeking a highly motivated Machine Learning Research Engineer to join our Embedded AI team to work on cutting-edge Large Language Model (LLM) research and development for Edge AI applications. As a key member of our team, you will lead the efforts on advancing the state-of-the-art in LLM architectures, Agentic LLM, and Reasoning LLM. Your work will involve exploring innovative approaches to integrate LLMs with domain knowledge, related tools, and other AI techniques to achieve human-like decision-making capabilities for business impact.

In this machine learning research engineer role, you’ll have the chance to work on the following topics:

Explore the application of LLMs in code generation, evaluation, and optimization

Conduct research and development on novel and efficient LLM architectures, training algorithms, and post-training algorithms

Design and develop new reasoning models with domain-specific knowledge

Design and implement advanced Agentic LLM system for complex task automations

Collaborate with system teams and internal business teams to define and implement AI/ML solutions for core business

Qualifications

Minimum requirements:

Doctoral degree in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Mechanical Engineering or related field

Cumulative 3.0/4.0 GPA or higher

Preferred qualifications:

Strong background in Natural Language Processing, Large Language Models, and Deep Learning frameworks

Proficiency in Python, C/C++, and software design, including debugging, performance analysis, and optimization

Excellent understanding of LLM architectures and transformer-based models

Experience with popular deep learning frameworks (e.g., PyTorch, JAX, ONNX) and LLM-specific libraries (e.g., transformers, trl, vllm)

Strong foundation in text processing, tokenization, and embedding techniques

Knowledge of few-shot learning, transfer learning, and fine-tuning

Knowledge of LLM performance evaluation

Knowledge of reinforcement learning, including policy gradient, DQN, DDPG

Experience with LLM agent implementation with tool calling

Experience with LLM post-training implementation, including PPO, DPO, and GRPO

Excellent communication and interpersonal skills, with the ability to work in a dynamic and distributed team