Sony
Sr. Research Engineer – AI/ML Image Processing & Computational Photography
$150K–$165K
San Jose · Senior · Full-time · Posted Sep 16, 2026
- Experience
- 3+ years of related experience may be considered for exceptional candidates with an MS; no years specified for the PhD route.
- Education
- PhD in Computer Science, Electrical Engineering, Computer Engineering, Applied Mathematics, Physics, or related field. Exceptional candidates with an MS and 3+ years of related experience may be considered.
About the job
Researches and develops AI/ML image-processing, computational-photography, and computer-vision algorithms from initial research through integration into Sony digital imaging products.
What you'll do
- Design, prototype, evaluate, benchmark, and improve algorithms and models for digital imaging.
- Document and communicate research findings, review emerging work, and draft invention disclosures and technical articles.
- Collaborate with product and engineering teams; occasionally travel to Japan for meetings.
What they ask for
- PhD in Computer Science, Electrical Engineering, Computer Engineering, Applied Mathematics, Physics, or a related field; exceptional candidates with an MS and 3+ years of related experience may be considered.
- Practical experience developing deep learning, machine learning, and/or AI algorithms from the ground up.
- Demonstrated experience in image/video processing, camera ISP, computational photography, low-level computer vision, or computer vision.
- Research record including peer-reviewed publications; proficiency in Python and PyTorch or a similar ML framework.
Nice to have
- Strong fundamentals in signal processing and statistical modeling.
- Familiarity with C/C++ and Linux.
Pay
USD 150,000 to USD 165,000 annual base salary; 10% annual bonus target (not stated as a total-pay range)
Tools
Python, PyTorch, C/C++, Linux
AI focus
Develops AI/ML methods and algorithms for image processing, computational photography, and computer vision in Sony digital imaging products, including dataset design, neural-network architecture development, model training, and evaluation.
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