Research Scientist Intern, Efficient World Foundation Models (PhD)
Burlingame, CAInternship$7,650–12,667/moSeen 1 day agoSeen in employer's feed 1 day ago
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Requirements
Credentials this posting asks for.
Job overview
Meta Reality Labs Research seeks a PhD intern to develop efficient AI algorithms and acceleration methods for wearable smart glasses. The internship focuses on foundational technologies for AI assistants and embodied AI, including world foundation models, multimodal understanding, generative models, and related areas. Internships last 12 to 24 weeks, with various start dates throughout the year.
Skills & qualifications
Skills
Qualifications
Full job description
Summary:
Meta’s mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Meta are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities — we're just getting started.Meta Reality Labs Research is looking for upcoming scientists and researchers with great interest in efficient AI algorithms and acceleration of AI machine learning applications for wearable smart glasses [Rayban Meta]. Our team specializes in developing state-of-the-art system optimized algorithms for machine perception. With the vision of always-on AI assistance, our team is focusing on cutting edge systems that can facilitate and bring AI assistants powered by LLMs to wearables systems. This internship will focus on aspects of foundational AI technologies around world foundation models, multitasking, multimodal understanding (audio, visual, spatial, etc), Generative models, or others that support AI assistant and embodied AI functionalities.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.
Required Skills:
Research Scientist Intern, Efficient World Foundation Models (PhD) Responsibilities:
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Research on design / model / execution of efficient ML algorithms.
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Develop and optimize novel ML, computational imaging, and generative AI algorithms for edge applications.
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Research on development and optimization of edge and distributed computing algorithms.
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Collaboration with and support of other researchers across various disciplines.
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Communication of research agenda, progress and results.
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Prototyping, building and characterizing experimental systems with real hardware.
Minimum Qualifications:
Minimum Qualifications:
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Currently has, or is in the process of obtaining a PhD in the fields of Computer Science, Electrical Engineering, or related field
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Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
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2+ years research experience in one or more of the following: developing machine learning and computer vision models, optimization of edge computing algorithms, or distributed compute architectures
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2+ years experience programming in Python/C++
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Experience with Deep Learning frameworks (Pytorch, TensorFlow, etc)
Preferred Qualifications:
Preferred Qualifications:
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Experience with customizing Multi-Modal LLM
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Experience with world foundation models, self-supervised learning, and embodied AI
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Experience with generative models and high-quality visual generation
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Experience in deploying ML algorithms for real-time execution on mobile and distributed platforms
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Experience with NAS or developing and deploying algorithms with hardware constraints
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Proven track record of achieving significant results, as demonstrated by first- and/or co-authored publications at leading workshops or conferences (e.g., CVPR, ICCV, TinyML, NeurIPS, ICML), patents, or grants
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Intent to return to the degree program after the completion of the internship
Public Compensation:
$7,650/month to $12,667/month + benefits
Industry: Internet
Equal Opportunity:
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [email protected].
How this job compares
Set against the 2,289 open Research Scientist jobs on Olive Jobs, counted October 29, 2026.
- Pay
- This posting$7,650–12,667/mo
- California median$180–270K/yr495 salary ranges
- US median$34–48/hr45 hourly ranges
- Openings
- Olive Jobs lists 713 open Research Scientist jobs in California and 2,289 across the US. 3% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 2 years of experience; Research Scientist postings that state a minimum ask for 5 years at the median (1,264 postings).
- Education
- This posting asks for a doctorate or professional degree; 821 of the 2,289 open Research Scientist jobs (36%) ask for a doctorate or professional degree.
Counted across the open, public US postings on Olive Jobs on October 29, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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