Texas Instruments · 2 months ago
Systems Engineering Intern (Device Learning)
EXKI 13560 N. Central Expwy, Dallas, TX, 75243, US
Internship
Onsite
InternTexas Instruments is a technology company that shapes the future through innovative products. They are seeking a highly motivated PhD student to join their Embedded AI team to work on cutting-edge on-device training research and development for resource-constrained microcontroller applications.
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
Master's Degree
PhD student
Experience in research and development of memory-efficient on-device training algorithms
Knowledge of supervised learning, unsupervised learning, and tiny reinforcement learning
Experience with hardware-aware training algorithm development
Ability to collaborate with internal systems and application engineers
Experience in prototyping and validating on-device training solutions
Knowledge of training-aware neural network architectures and reinforcement learning agents
Familiarity with memory-efficient backpropagation techniques, gradient compression methods, sparse training approaches, and incremental learning strategies
Experience in designing and implementing software frameworks and tools for on-device training deployment
Ability to interface with application teams, customers, and internal stakeholders
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
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