Junior Research Scientist in Edge-AI and Embodied-AI Systems
Develop next-generation AI systems for energy-efficient, secure, and robust embodied and edge AI applications.
Best for:Best for AI/ML engineers with expertise in tiny-LLMs, multimodal AI, and system-level optimizations, seeking to work on cutting-edge embodied and edge AI applications in a collaborative research environment.
Overview
This position at New York University Abu Dhabi’s eBRAIN Lab focuses on advancing Edge-AI and Embodied-AI systems with tiny-LLMs, tiny-VLMs, and Agentic-AI frameworks. The successful candidate will contribute to research and development of high-performance, energy-efficient AI solutions targeting real-world applications in autonomous systems, healthcare, and robotics. The role involves designing, prototyping, and optimizing AI systems while collaborating with a multidisciplinary team of researchers, engineers, and students. The eBRAIN Lab aims to embed energy-efficient and secure AI technologies into cyber-physical systems, fostering innovation in assistive cognitive technologies. This is an opportunity for hands-on engineering, full-system prototyping, and high-impact publication in top AI venues.
What you'll do
- Design, implement, and optimize next-generation Edge-AI and Embodied-AI systems using tiny-LLMs, tiny-VLMs, and Agentic-AI frameworks.
- Develop and prototype full-system AI solutions targeting real-world applications in autonomous systems, healthcare, and robotics.
- Conduct research on novel methods for robust, energy-efficient, and secure AI systems, including addressing hallucination, safety, and alignment issues.
- Collaborate with a multidisciplinary team of researchers, engineers, and students to investigate and develop cutting-edge AI methods.
- Generate intellectual property (IP) and contribute to high-quality publications in top AI venues.
- Build and optimize TRL-4/5 prototypes for real-world use cases, focusing on high performance and energy efficiency.
- Engage in hands-on engineering work, including system-level optimizations and deployment of AI solutions.
- Participate in cross-validation of AI methods across diverse application domains such as autonomous vehicles, UAVs, robotics, and healthcare.
Requirements
Degree required: Bachelor’s or Master’s degree in Computer Engineering, Computer Science, Electrical Engineering, or a related field
Language: English; proficiency required for collaboration and communication
Instruction: English
Publications: Publications in top ML venues are highly preferred but not mandatory
- Extensive knowledge of Robotics, ML, AI, Deep Neural Networks (DNNs), LLMs/VLMs, and Multimodal LLMs
- Hands-on experience in practical AI/ML system development and system-level optimizations
- Proficient in ML frameworks such as PyTorch and TensorFlow
- Experience with MLOps, robust/secure computing, hallucination and alignment, and privacy-preserving ML
- Strong problem-solving, analytical, communication, and writing skills
- Less than 5 years of professional experience
- Less than 5 years of professional experience post-degree
- No specific nationality restrictions, though Emirati candidates are encouraged to apply
Funding & terms
How to apply
Deadline: 2026-10-31 · open until filled
Applicants must submit a cover letter, CV with engineering/programming skills, professional experience and publications list, university degrees/transcripts, 1-page summaries of BS/MS theses and professional experience, a 1-page letter of motivation, 1-page engineering accomplishments, and 2 letters of recommendation. An envisioned 2-year work plan may be provided after shortlisting. All documents must be submitted in PDF format. Visit the NYUAD careers page for detailed instructions.
- Cover letter
- Curriculum Vitae (CV) with engineering and programming skills
- Full list of professional/industrial experience and scientific publications
- University degrees and transcripts (or letter regarding tentative completion date)
- 1-page executive summaries of BS and/or MS Theses
- 1-page summary of professional experience and future growth plans
- 1-page of Engineering and/or R&D accomplishments and interests
- 1-page letter of Motivation
- At least 2 letters of recommendation with contact information
- An envisioned workplan over 2 years (optional, provided after shortlisting)
- Download links for PDFs of BS and/or MS Theses (if available)
Contact
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