Senior Staff Research Engineer, Neural Network Video Coding
OfinnoReston, VA
Automate your job search with Sonara.
Submit 10x as many applications with less effort than one manual application.1
Reclaim your time by letting our AI handle the grunt work of job searching.
We continuously scan millions of openings to find your top matches.
Overview
Schedule
Full-time
Career level
Senior-level
Benefits
Health Insurance
Paid Sick Leave
Flexible/Unlimited PTO
Job Description
Senior Staff Research Engineer, Neural Network Video CodingAbout Ofinno:Ofinno is a leading research and development lab headquartered in Reston, Virginia, specializing in advancing communication and media standards. Our team’s innovative work has led to significant contributions to technologies such as 5G cellular, Wi-Fi, and media compression. Ofinno holds strategic partnerships and licensing agreements with several of the world’s leading technology companies that use such technologies. At Ofinno, we foster an environment of collaboration and excellence, where researchers can focus on delivering breakthroughs that shape the future of technology.Position Overview:As a Senior Staff Research Engineer in the Advanced Media Lab, you will serve as the tech stack owner for our neural network-based video coding (NNVC) efforts and lead related standardization activities. You will set the research direction with cross-team impact, turning neural coding ideas into reproducible evidence, standards-ready proposals, and patentable inventions.Key Responsibilities:As a Senior Staff Research Engineer in NNVC, you will:
- Own NNVC technical direction end-to-end, from research framing to standards-ready proposals.
- Collaborate cross-team to remove blockers, integrate tool interactions, and improve shared infrastructure (e.g., testing pipelines, scripts, datasets, and configurations).
- Communicate research findings and technical insights to clients, partners, and industry audiences, representing the company’s expertise in video compression.
- Drive standards contributions by authoring technical proposals, defending results, and working with delegates/partners to align test conditions, baselines, and conclusions.
- Invent and develop patentable solutions that improve compression efficiency and/or complexity, and support the full IP process- from invention disclosure through filing.
- Implement and optimize GPU-accelerated training and/or inference workflows for NNVC tools, including profiling and performance tuning for latency, throughput, and memory.
- Mentor and multiply team output through code reviews, technical coaching, interview participation, and onboarding others to NNVC methodology and best practices.
- Ph.D. or M.S. (EE/CS or related) with 7+ years of relevant research and/or product experience in video compression and/or NNVC.
- Prior delegate or active contributor experience in video coding standardization bodies, including JVET, MPEG, or AOM.
- Deep expertise in video compression plus strong capability in machine learning for vision/video (model design, training, evaluation, and failure analysis).
- Solid understanding of at least one modern video coding standard, including HEVC/H.265, VP9, VVC/H.266, AV1, AV2, or ECM.
- Proven ability to act as an architect/tech stack owner: make sound technical decisions, set direction, and drive execution through measurable outcomes.
- Strong implementation skills across the stack: PyTorch (or equivalent) for training/inference workflows and C/C++ for codec prototyping and/or production integration.
- Strong written and verbal communication skills, including the ability to represent the company in standards meetings and industry events.
- Track record of technical impact through peer-reviewed publications, patents, and/or standards contributions.
- Hands-on experience in one or more video coding tool areas, including neural in-loop filtering, neural prediction, learned transforms, or end-to-end neural video compression.
- Hands-on experience with GPU acceleration, including performance tuning and deployment considerations.
- Familiarity with the patent filing process and collaborating with patent attorneys.
What else you should know Our people are our business. We know you have to see it to believe it, but here are some of the perks you can count on:
- 401(K) matching -- We help you plan and save for retirement with a 401(K) matching program that’s available on day one.
- Free healthcare plans -- Ofinno covers full premiums for you are your family on select healthcare plans, including employer HSA contributions if applicable.
- Free Food -- Our kitchen is always fully stocked, including lunch, protein bars, fruit, sodas, coffee, and tea.
- Unlimited Paid Time Off -- Our lives are enriched by family time, vacations, and personal time. We offer unlimited paid time off and sick leave.
- On-campus gym -- Unwind, reduce stress and feel great – even when you’re at work.
- Other benefits, too long to list -- Please discuss with our great People Ops team about additional benefits offered.
Powered by JazzHR
Automate your job search with Sonara.
Submit 10x as many applications with less effort than one manual application.
FAQs About Senior Staff Research Engineer, Neural Network Video Coding Jobs at Ofinno
What is the work location for this position at Ofinno?
This job at Ofinno is located in Reston, VA, according to the details provided by the employer. Some roles may also include multiple work locations depending on the requirement.
What pay range can candidates expect for this role at Ofinno?
Employer has not shared pay details for this role.
What employment applies to this position at Ofinno?
Ofinno lists this role as a Full-time position.
What experience level is required for this role at Ofinno?
Ofinno is looking for a candidate with "Senior-level" experience level.
What is the process to apply for this position at Ofinno?
You can apply for this role at Ofinno either through Sonara's automated application system, which helps you submit applications 10X faster with minimal effort, or by applying manually using the direct link on the job page.