Systems Engineering Intern (Device Learning)

Texas Instruments • Dallas, TX

14d

About The Position

We can’t predict what the future holds, but we know Texas Instruments will have a part in shaping it. At TI, Systems engineers focus deeply on understanding the technical needs, and future trends of an industry or end equipment, then create new products and innovative forward-looking product roadmaps to solve them. Systems Engineers are an integral part each phase of new product development at TI. In the early stages of product development, systems engineers interface with key stakeholders (customer decision-makers, application engineers, marketing, management, sales, IC design engineers, technology development) to negotiate specifications, perform trade-offs, understand the competitive landscape, and ultimately develop detailed technical definitions for new products. They then collaborate with the full IC development team (design, applications, test, product engineers) to deliver products to the market which are compelling, competitive, cost-conscious, manufacturable, and importantly, successful in growing TI’s business. We are seeking a highly motivated PhD student to join our Embedded AI team in Kilby Labs this summer to work on cutting-edge on-device training research and development for resource-constrained microcontroller applications. As a key member of our team, you will focus on enabling learning and adaptation capabilities directly on edge devices with strict power and cost constraints. Your work will involve exploring innovative approaches to co-optimize training algorithms, memory management, and hardware architectures to make on-device learning practical for embedded systems across diverse real-world applications. In this systems engineering intern role, you’ll have the chance to: Research and develop memory-efficient on-device training algorithms optimized for microcontroller-class devices, including techniques for supervised learning, unsupervised learning, and tiny reinforcement learning that enable local model adaptation and personalization without extensive cloud connectivity Explore hardware-aware training algorithm development through system-level co-design, creating novel training methods specifically optimized for future generations of TI's low-power processors and accelerators Apply edge training techniques to practical real-world applications on TI platforms Collaborate with internal systems and application engineers to prototype and validate on-device training solutions through simulation, hardware evaluation, and real-world deployment scenarios Develop and optimize training-aware neural network architectures and reinforcement learning agents specifically tailored for resource-constrained edge platforms with limited memory and compute resources Investigate memory-efficient backpropagation techniques, gradient compression methods, sparse training approaches, and incremental learning strategies for embedded systems Participate in the design and implementation of software frameworks and tools for on-device training deployment, benchmarking, and performance analysis across different application domains Interface with application teams, customers, and internal stakeholders to understand real-world use cases, define system requirements for adaptive edge AI solutions, and identify opportunities for on-device learning in TI's product portfolio Put your talent to work with us as a systems engineering intern – change the world, love your job!

Requirements

• Currently pursuing a graduate degree in Electrical Engineering, Computer Engineering, Electrical and Computer Engineering or related field
• Cumulative 3.0/4.0 GPA or higher

Nice To Haves

• Solid background in machine learning, embedded systems, computer architecture, and/or edge AI, with particular interest in on-device training, reinforcement learning, or resource-constrained learning systems
• Proven track record of research in on-device learning, federated learning, continual learning, efficient training methods, or tiny ML as demonstrated by first-authored publications at leading ML/systems conferences (e.g., NeurIPS, ICML, MLSys, ICLR, ASPLOS, MobiCom, TinyML)
• Strong programming skills in Python and C/C++, with experience in embedded software development and optimization for resource-constrained platforms
• Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) and embedded ML deployment (e.g., TensorFlow Lite, TinyML frameworks)
• Knowledge of training optimization techniques including gradient compression, quantization-aware training, memory-efficient backpropagation, and low-rank adaptation methods
• Understanding of reinforcement learning fundamentals and experience with lightweight RL algorithms suitable for embedded deployment
• Familiarity with unsupervised and self-supervised learning techniques that can operate with limited labeled data on edge devices
• Understanding of neural network pruning, knowledge distillation, model compression techniques, and neural architecture search
• Experience with microcontroller platforms (e.g., ARM Cortex-M series, RISC-V) and real-time embedded systems programming
• Knowledge of fixed-point arithmetic, low-precision training methods, and mixed-precision optimization
• Experience with algorithm-hardware co-design and performance-power trade-off analysis for edge AI systems
• Familiarity with control systems, feedback loops, signal processing, or sensing applications
• Excellent communication and interpersonal skills, with the ability to work in a dynamic and distributed team
• Ability to establish strong relationships with key stakeholders critical to success, both internally and externally
• Strong verbal and written communication skills to audiences of varied background
• Ability to simplify complex problems and navigate uncertainty
• Ability to quickly ramp on new systems and processes
• Demonstrated strong interpersonal, analytical and problem-solving skills
• Ability to work in teams and collaborate effectively with people in different functions
• Ability to take the initiative and drive for results
• Strong time management skills that enable on-time project delivery

Responsibilities

• Research and develop memory-efficient on-device training algorithms optimized for microcontroller-class devices, including techniques for supervised learning, unsupervised learning, and tiny reinforcement learning that enable local model adaptation and personalization without extensive cloud connectivity
• Explore hardware-aware training algorithm development through system-level co-design, creating novel training methods specifically optimized for future generations of TI's low-power processors and accelerators
• Apply edge training techniques to practical real-world applications on TI platforms
• Collaborate with internal systems and application engineers to prototype and validate on-device training solutions through simulation, hardware evaluation, and real-world deployment scenarios
• Develop and optimize training-aware neural network architectures and reinforcement learning agents specifically tailored for resource-constrained edge platforms with limited memory and compute resources
• Investigate memory-efficient backpropagation techniques, gradient compression methods, sparse training approaches, and incremental learning strategies for embedded systems
• Participate in the design and implementation of software frameworks and tools for on-device training deployment, benchmarking, and performance analysis across different application domains
• Interface with application teams, customers, and internal stakeholders to understand real-world use cases, define system requirements for adaptive edge AI solutions, and identify opportunities for on-device learning in TI's product portfolio

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees