Video/Image AI/ML Software Engineer Intern (Multimedia) - 2027 Start (PhD)

About the team Our team is building industry leading, highly efficient and scalable video codec hardware solutions (FPGA and ASIC) from the ground up to better serve our billions of users.

We are looking for strong software and firmware engineers to design algorithms, C-model, and firmware for advanced video encoding and processing implemented in dedicated hardware accelerators.

The successful candidate will be part of a fast growing team that includes algorithm, architecture, software, firmware, and hardware design and verification experts with a dedication to technical excellence and a passion to build large-scale and high-performing video platforms and services.

We are looking for talented individuals to join us for an internship.

PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis, so we encourage you to apply early.

Please clearly state your availability in your resume (Start date, End date).

Responsibilities: - Conduct research and development on video analysis algorithms, including but not limited to region-of-interest (ROI) detection, content understanding, feature extraction, and perceptual quality assessment. - Support the design and implementation of image/video quality evaluation methods, including both objective and learning-based approaches. - Assist in building and curating datasets, benchmarks, and evaluation pipelines for multimedia quality and content analysis tasks. - Contribute to end-to-end video quality optimization solutions by combining video analysis, enhancement, processing, and encoding techniques, such as frame-level rate control and perceptual optimization strategies. - Apply developed algorithms to VOD and live streaming use cases, including quality monitoring, data analysis, objective evaluation, and performance validation. - Prototype, train, and optimize machine learning / deep learning models for multimedia understanding and quality-related tasks. - Collaborate with cross-functional teams, including infrastructure, platform, and engineering teams, to help integrate research prototypes into production workflows. - Participate in offline experiments, online validation, and A/B testing analysis to measure algorithm impact and guide iteration. - Document research findings, experimental results, and technical insights, and communicate them clearly to both research and engineering stakeholders.

Minimum Qualifications - Currently pursuing a PhD degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related technical field. - Strong programming, debugging, and optimization skills in one or more general-purpose programming languages, such as C/C++ and/or Python. - Solid understanding of image/video analysis or image/video quality assessment methods. - Familiarity with commonly used objective quality metrics and related techniques, such as VMAF, PSNR, SSIM, or similar approaches. - Hands-on experience with machine learning or deep learning and familiarity with frameworks such as PyTorch or TensorFlow. - Ability to conduct experiments independently, analyze results rigorously, and iterate based on data. - Good written and verbal communication skills, with the ability to work effectively in a collaborative research and engineering environment.

Preferred Qualifications - Research or hands-on experience with Transformer architectures, multimodal large models (MLLMs), or vision-language/video-language modeling. - Familiarity with video processing and enhancement algorithms, such as super-resolution, restoration, denoising, or diffusion-based approaches. - Understanding of video codecs and compression standards, such as AVC, HEVC, AV1, and VP9, with exposure to video codec design or optimization preferred. - Experience in perceptual video optimization, learned compression, or quality-driven encoding strategies. - Familiarity with multimedia system deployment, production experimentation, or large-scale evaluation pipelines. - Exposure to embedded systems, processors such as ARM or RISC-V, or real-time development in RTOS environments is a plus. - Demonstrated passion for turning advanced research into impactful real-world applications.