Research Scientist Intern, On-Device AI Model Optimization (PhD)
Burlingame, CAInternship$7,650–12,667/moSeen 1w agoSeen in employer's feed 1 day ago
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At a glance
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Requirements
Credentials this posting asks for.
Job overview
Meta Reality Labs seeks PhD interns to research and develop efficient machine learning models for AR/VR applications under on-device compute, memory, power, and real-time constraints. Interns will work across algorithms, user experiences, and hardware execution, collaborating with research, software, and hardware teams to develop and deploy neural networks. Internships last 12 to 24 weeks, with start dates throughout the year.
Skills & qualifications
Skills
Qualifications
Full job description
Summary:
Reality Labs advances Meta's vision for virtual reality (VR) and augmented reality (AR). Delivering compelling experiences on AR/VR devices requires innovation and co-design across the full technology stack, from algorithms to custom silicon. Our teams push the state of the art in computer vision, speech, virtual assistants, machine learning, mixed reality, graphics, displays, sensors, and human body tracking.We are seeking interns with a background in developing efficient machine learning models for AR/VR applications, including computer vision, speech, sensor fusion, natural language processing (NLP), and multimodal AI. Our team develops high-performing models within tight compute, memory, and power constraints, using optimization techniques such as sparsity, quantization, compression, neural architecture search, and tensor decomposition. Ideal candidates will have practical experience building real-time models that maintain high accuracy under on-device deployment constraints.In this role, you will gain experience across the full stack, from user experiences and algorithms to hardware execution. You will collaborate with domain experts to understand application challenges and develop state-of-the-art neural networks to address them, then partner with software and hardware teams to deploy these solutions on-device.Our internships range from twelve (12) to twenty-four (24) weeks, with start dates throughout the year.
Required Skills:
Research Scientist Intern, On-Device AI Model Optimization (PhD) Responsibilities:
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Define, plan, and execute cutting-edge deep learning research to advance AR/VR experiences.
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Develop novel deep learning techniques to achieve state-of-the-art accuracy for large language models (LLMs), multimodal models, and reasoning models within on-device resource and real-time execution constraints.
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Collaborate with research scientists and software engineers to develop model optimization techniques, such as quantization, compression, and pruning, for vision, speech, user interfaces, and other applications.
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Partner with software and hardware engineers to develop trade-off curves between model accuracy and runtime requirements, including latency and energy consumption.
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Clearly communicate experimental results and recommendations to the team and cross-functional partners.
Minimum Qualifications:
Minimum Qualifications:
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Currently holds or is pursuing a PhD in Computer Science, Electrical Engineering, or a 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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Experience developing machine learning solutions
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Programming experience and experience using deep learning frameworks to prototype ideas
Preferred Qualifications:
Preferred Qualifications:
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A track record of first-author publications at top-tier machine learning or AI conferences, such as NeurIPS, ICML, ICLR, ACL, AAAI, or similar venues
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Experience training and running inference with LLMs, multimodal models, or reasoning models
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Experience with one or more model optimization techniques, such as quantization, pruning, distillation, parameter-efficient fine-tuning (PEFT), or speculative decoding, for cloud or on-device AI deployment
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Demonstrated research and software engineering experience through internships, coding competitions, or contributions to open-source repositories, such as those hosted on GitHub
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 2,306 open Research Scientist jobs on Olive Jobs, counted October 30, 2026.
- Pay
- This posting$7,650–12,667/mo
- California median$183–270K/yr503 salary ranges
- US median$34–48/hr45 hourly ranges
- Openings
- Olive Jobs lists 722 open Research Scientist jobs in California and 2,306 across the US. 3% of them nationwide are listed as remote or flexible.
- Experience
- Research Scientist postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 2 to 6 years (1,277 postings).
- Education
- This posting asks for a doctorate or professional degree; 832 of the 2,306 open Research Scientist jobs (36%) ask for a doctorate or professional degree.
Counted across the open, public US postings on Olive Jobs on October 30, 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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