Machine Learning Research Engineer

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

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

Why TI?

• Engineer your future. We empower our employees to truly own their career and development. Come collaborate with some of the smartest people in the world to shape the future of electronics.
• We're different by design. Diverse backgrounds and perspectives are what push innovation forward and what make TI stronger. We value each and every voice, and look forward to hearing yours. Meet the people of TI
• Benefits that benefit you. We offer competitive pay and benefits designed to help you and your family live your best life. Your well-being is important to us.

About Texas Instruments Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures and sells analog and embedded processing chips for markets such as industrial, automotive, personal electronics, communications equipment and enterprise systems. At our core, we have a passion to create a better world by making electronics more affordable through semiconductors. This passion is alive today as each generation of innovation builds upon the last to make our technology more reliable, more affordable and lower power, making it possible for semiconductors to go into electronics everywhere. Learn more at TI.com.
Texas Instruments is an equal opportunity employer and supports a diverse, inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, disability, genetic information, national origin, gender, gender identity and expression, age, sexual orientation, marital status, veteran status, or any other characteristic protected by federal, state, or local laws.
If you are interested in this position, please apply to this requisition.