Systems Engineering Intern (CIM)

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 digital compute in-memory (CIM) research and development for resource-constrained microcontroller applications. As a key member of our team, you will focus on advancing energy-efficient computing architectures that enable intelligent edge processing on cost and power-constrained platforms. Your work will involve exploring innovative approaches to co-optimize algorithms, architectures, and hardware implementations for CIM systems. In this systems engineering intern role, you’ll have the chance to: - Research and develop novel digital compute in-memory architectures optimized for edge AI workloads on microcontrollers - Explore algorithm-hardware co-design techniques to maximize energy efficiency and computational throughput within strict power and cost constraints - Collaborate with internal technology design teams to simulate and validate CIM-based solutions - Develop and optimize machine learning models and inference engines specifically tailored for in-memory computing paradigms - Participate in the design and implementation of software toolchains and simulation frameworks for CIM evaluation - Investigate memory hierarchy optimizations and data movement reduction strategies for edge processing Put your talent to work with us as a systems engineering intern – change the world, love your job! . Minimum 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 Preferred Qualifications: - Solid background in computer architecture, digital design, memory systems, and/or embedded systems - Proven track record of research in compute in-memory architectures, processing-in-memory (PIM), or near-memory computing as demonstrated by first-authored publications at leading architecture/systems conferences (e.g., ISCA, MICRO, HPCA, DAC, ISSCC) - Proficiency in hardware description languages (Verilog/VHDL) and/or architectural simulation tools - Strong programming skills in Python and C/C++, including performance analysis and optimization - Experience with machine learning model optimization, quantization, and deployment on resource-constrained devices - Knowledge of SRAM/DRAM circuit design and memory architecture principles - Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow) and embedded ML libraries (e.g., TensorFlow Lite, CMSIS-NN) - Understanding of energy-efficient computing techniques and power modeling - Experience with algorithm-hardware co-design methodologies - 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