Texas Instruments · 2 weeks ago
Systems Engineering Intern (Device Learning)
Dallas, TX
Internship
Onsite
InternTexas Instruments is a global semiconductor company that designs, manufactures and sells analog and embedded processing chips. They are seeking a Systems Engineering Intern to work on cutting-edge on-device training research and development for resource-constrained microcontroller applications, focusing on enabling learning capabilities directly on edge devices.
ComputerDSPSemiconductor
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
Qualification
Required
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
Preferred
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
Benefits
Competitive pay and benefits designed to help you and your family live your best life
Company
Texas Instruments
Texas Instruments is a global semiconductor company that manufactures, designs, tests, and sells embedded and analog processing chips.
Founded in 1930
10001+ employees
H1B Sponsorship
Texas Instruments has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (189)
2024 (184)
2023 (148)
2022 (222)
2021 (165)
2020 (179)
Funding
Current Stage
Public CompanyTotal Funding
$13.61BKey Investors
U.S. Department of Commerce
2025-05-20Post Ipo Debt· $1.2B
2024-12-20Grant· $1.61B
2024-05-28Post Ipo Equity· $2.5B
Leadership Team
Richard Templeton
President and Chief Executive Officer
Krunali Patel
Senior vice president and chief information officer
Recent News
2026-01-09
2026-01-07
Company data provided by crunchbase