Meghana Reddy

Firmware Engineer @ Formula Slug

About

I am a computer engineering student with a strong interest in firmware and embedded systems, particularly low-level software and hardware–software integration. My experience includes testing, debugging, and validating system behavior through coursework and internships, with a focus on performance and reliability in real-world environments.

Country

United States

City

San Ramon

Industry

Computer Software

Skill

Embedded Software, C++, Embedded Devices, Neural Networks, OpenCV, Facial Recognition, React.js, Real-time Monitoring, Full-Stack Development, Hardware Installation, Raspberry Pi, Retrieval-Augmented Generation (RAG), Artificial Intelligence (AI), Computer Hardware, Operating Systems, Machine Learning, Algorithm Development, Python (Programming Language)

Experience

Firmware Engineer

Formula Slug

2025-9 - Present · 1 yr 1 mo

United States

• Developed embedded C firmware and low-level hardware drivers for an automotive battery management system (BMS) • Integrated the LTC6810 Battery Monitor IC to enable multi-cell voltage measurement and temperature sensing for battery safety and diagnostics. • Implemented state-of-charge (SoC) estimation algorithms using voltage and current telemetry for battery diagnostics and energy management • Performed firmware debugging, hardware validation, and system integration testing using schematics, datasheets, and oscilloscope-based signal analysis

President

UCSC Quantum Computing

2025-9 - Present · 1 yr 1 mo

- Founded and lead a research-focused student organization studying quantum computing applications in cybersecurity - Direct collaborative research paper development among club members - Established connections with professors and students across institutions - Organized fundraising initiatives to support research and academic events

Research Assistant

Neuromorphic Computing Group

2025-3 - 2026-4 · 1 yr 2 mos

• Conducted research on routing algorithms for spiking neural networks (SNNs) within neuromorphic hardware architectures • Investigated AI models optimized for hardware-constrained environments, focusing on latency, throughput, and computational efficiency • Analyzed hardware-aware machine learning strategies for efficient deployment on neuromorphic accelerators

Intern

Sagesse

2025-6 - 2025-8 · 3 mos

• Optimized system performance and runtime stability through profiling, debugging, and targeted code optimization • Resolved performance bottlenecks across system modules through structured testing and root-cause analysis