Summary 

Singapore's Embodied AI Centre (EAIC) is a new national initiative under the RIE2030 Manufacturing, Trade and Connectivity domains to establish Singapore as a leading robotics and embodied AI hub. EAIC brings together research and industry partners to build engineering and R&D capabilities, advance trusted evaluation, benchmarking and validation of embodied AI models and robotics solutions, and promote an open and interoperable ecosystem that accelerates adoption in real-world applications. 

We are looking for AI / Robotics Research Scientists who will develop intelligent robotic systems that can perceive, reason and act in dynamic environments. Working closely with software engineers, researchers and industry partners, the role will contribute to the design, integration and deployment of embodied AI solutions for real-world applications.  

Job Description 

• Develop and integrate AI and robotics solutions for real-world testing and deployment

• Develop scalable infrastructure for data ingestion, training, and iteration 

• Design and run large-scale training pipelines for multimodal / VLA systems 

• Own the full AI/ML loop: data -> training -> evaluation -> deployment on real robots 

• Design and implement algorithms for perception, decision-making, navigation and robotic system control

• Conduct experiments, analyse performance and improve system robustness in real-world environments

• Work hands-on with robots to debug, iterate, and improve behaviour

• Collaborate with software engineers, researchers and partners to translate research into deployable solutions

• Continuously improve performance across real-world tasks and environments 

Job Requirements 

• Built and trained large-scale models (LLMs, VLMs, or robot foundation models) 

• Demonstrated ability to develop AI, machine learning, robotics or autonomous system solutions through projects, research or professional work. 

• Proficiency in Python, C++, and PyTorch for AI and robotics development

• Experience applying machine learning, computer vision, robot control, planning or related techniques to solve real-world problems

• Strong understanding of ML techniques like attention optimization, kernel fusion, and efficient memory usage 

• Proven experience setting up and scaling distributed training across hundreds of GPUs 

• Strong understanding of parallelization strategies (data, model, pipeline parallelism) 

• Ability to work with sensor, image or other real-world data to develop and evaluate intelligent systems