VLM Research Engineer
4 days ago
Role Overview
The VLM Research Engineer specializes in Vision–Language Models (VLMs), enabling robots and autonomous vehicles to understand natural language instructions, ground perception, and translate commands into actionable tasks. This role is pivotal in advancing multimodal AI systems, bridging vision and language for real-world autonomy.
Key Responsibilities
- Research, design, and implement state-of-the-art Vision–Language Models for robotics and autonomous vehicles.
- Enable robots/vehicles to understand natural language instructions and ground them in perception data.
- Collaborate with cross-functional teams (AI, robotics, perception, and software) to integrate VLMs into autonomous systems.
- Develop algorithms for instruction following, scene understanding, and multimodal reasoning.
- Evaluate model performance using benchmarks, real-world datasets, and simulation environments.
- Stay updated with latest research in vision-language models, multimodal AI, and robotics.
- Document research findings, model architectures, and best practices for internal and external dissemination.
Required Qualifications
- Master's or PhD in Computer Science, AI, Robotics, or related field with specialization in deep learning or multimodal AI.
- 3+ years of experience in research and development of vision-language models or multimodal AI systems.
- Strong expertise in deep learning frameworks (PyTorch, TensorFlow) and transformer-based architectures.
- Experience in applying VLMs to robotics, autonomous vehicles, or related real-world systems is preferred.
- Strong programming skills in Python and C++.
- Excellent problem-solving, analytical, and communication skills.
Desired Competencies
- Deep understanding of multimodal learning, vision-language grounding, and instruction following.
- Ability to innovate and adapt cutting-edge AI research to practical robotics applications.
- Experience with simulation environments and real-world robotic platforms.
- Strong collaboration skills to work with cross-functional research and engineering teams.
- Publication record in relevant conferences/journals is a plus.
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