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Systems Engineering Intern (Machine Learning) at Texas… · Dallas
InternshipOn-siteFull-timeDallas, TX
Skills
natural language processinglarge language modelsdeep learning frameworkspythonc/c++llm architecturespytorchtransfer learning
Job Description
Summary: Texas Instruments is a global semiconductor design and manufacturing company that develops analog and embedded processing ICs. They are seeking a highly motivated PhD student to join their Generative AI team as a Systems Engineering Intern to work on cutting-edge Large Language Model (LLM) research and development for Edge AI applications, focusing on advancing LLMs and Agentic LLMs with applications in coding generation and optimization.
Responsibilities:
- Research and develop novel LLM architectures and training methods to improve performance and efficiency
- Explore the application of LLMs in coding generation, optimization, and other areas of interest
- Collaborate with internal business teams to define and implement AI strategies for our products
- Develop and maintain large-scale deep learning systems, incorporating LLMs and other AI techniques
- Participate in the design and implementation of advanced Agentic LLM system
Required Qualifications:
- Currently enrolled in a PhD program in Computer Science, Electrical and Computer Engineering, or related fields
- Cumulative 3.0/4.0 GPA or higher
Preferred Qualifications:
- Solid background in Natural Language Processing, Large Language Models, and Deep Learning frameworks
- Proven track record of designing, developing, and deploying machine learning models/LLMs as demonstrated by first-authored publications at leading AI/ML workshops or conferences
- 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)
- Knowledge of few-shot learning, transfer learning, and fine-tuning
- Knowledge of LLM performance evaluation
- Knowledge of reinforcement learning for LLM; Knowledge of LLM post-training implementation, including PPO, DPO, and GRPO
- Experience with Agentic AI implementation with tool calling
- Excellent communication and interpersonal skills, with the ability to work in a dynamic and distributed team
Required Skills: Natural Language Processing, Large Language Models, Deep Learning frameworks
Important Skills: Python, C/C++, LLM architectures, PyTorch, transfer learning
Benefits: Competitive pay and benefits designed to help you and your family live your best life
Benefits
Competitive pay and benefits designed to help you and your family live your best life