Internship - Multimodal Embodied AI

merl; Cambridge (MA); On-site; USD 28,000 - 41,000; Part time

Job summary

MERL is seeking a self-motivated intern to tackle problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate is a PhD student with a strong ML/vision background and top-tier publications.

Experience designing synthetic scenes using graphics software, and working with simulators like Habitat/SoundSpaces, SMPL models, and reinforcement learning is preferred.

Qualifications

• PhD-level candidate in ML/vision required.
• Experience designing 3D interactive scenes.
• Strong publication record in CVPR/NeurIPS or similar venues.
• Experience with simulators Habitat/SoundSpaces and SMPL models.

Responsibilities

• Collaborate with MERL computer vision researchers to develop algorithms.
• Prepare manuscripts for scientific publications.
• Conduct experiments in dynamic indoor environments and report results.

Skills

3D scene design

Vision-based embodied AI

Multimodal LLMs

Reinforcement learning

Programming fundamentals

Publications in top venues

Education

PhD student

Tools

Habitat

SoundSpaces

SMPL

Job description

MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience in designing 3D interactive scenes
• Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
• Experience training large language models on multimodal data
• Experience with training reinforcement learning algorithms
• Strong foundations in machine learning and programming
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics.

In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expresses prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.