HOMEE AI

Benefits:

• daily breakfast

• snacks

• drinks

• access to a fitness center

• yoga room

Education Requirements:

• Master’s or PhD in Computer Science, AI, or a related field

Experience Requirements:

• At least 3 years of experience in NLP, with a significant focus on Large Language Models

Other Requirements:

• Proven track record of implementing LLMs in production environments, especially models based on the Transformer architecture

• Strong programming skills in Python and familiarity with NLP libraries (Transformers, Hugging Face)

• Experience with multi-agent system architectures and their applications in AI

Responsibilities:

• Design, develop, and optimize Large Language Models for understanding and generating human-like text, focusing on applications in spatial planning and design

• Implement and manage multi-agent communication frameworks that leverage LLMs to facilitate intelligent and context-aware interactions

• Innovate and research new methodologies for scaling LLMs while maintaining efficiency and accuracy

• Collaborate with product teams to integrate advanced LLM solutions into our platform, ensuring alignment with business objectives

• Lead and mentor a team of NLP engineers, fostering an environment of learning and innovation

Benefits:

• daily breakfast

• snacks

• drinks

• access to a fitness center

• yoga room

Education Requirements:

• Master’s or PhD in Computer Science, Artificial Intelligence, or a related field

Experience Requirements:

• Minimum of 5 years of experience in developing and deploying reinforcement learning systems

Other Requirements:

• Proven track record with modern RL techniques and frameworks (e.g., RLLib, TensorFlow Agents)

• Experience in integrating outputs from Large Language Models into reinforcement learning training environments

• Strong programming skills in Python and familiarity with AI development environments

Responsibilities:

• Develop and optimize reinforcement learning models that drive our AI-based placement engine, improving efficiency and accuracy in furniture arrangement and space utilization

• Integrate LLM outputs to augment and enhance the training datasets for RL models, ensuring a rich, context-aware learning environment

• Lead projects to test and scale RL solutions, ensuring they meet the functional and performance criteria required for real-world applications

• Collaborate with cross-functional teams, including NLP engineers and design experts, to ensure that RL models effectively incorporate domain-specific knowledge and customer preferences

• Stay updated with the latest research and technologies in RL and LLMs, applying best practices and innovations to advance our AI capabilities

Benefits:

• daily breakfast

• snacks

• drinks

• access to a fitness center

• yoga room

Education Requirements:

• Bachelor’s or Master’s in Computer Science, Electrical Engineering, or related field

Experience Requirements:

• 5+ years of experience in software development, with a strong focus on 3D data processing and algorithm optimization

Other Requirements:

• Proficiency in programming languages such as Python, C++, CUDA, and experience with deep learning frameworks (TensorFlow, PyTorch, Keras)

• Strong analytical skills and experience with large datasets, preferably in the context of iOS data, 3D point clouds, or multi-modal sensor fusion

Responsibilities:

• Develop and optimize algorithms for Gaussian Splatting, Semantic/Instance Segmentation, and 3D Mesh Surface Reconstruction using data from various sources, including iOS devices and multi-modal sensor data

• Integrate and improve algorithms within production-level software, focusing on data fusion, object detection, and 3D point cloud processing

• Implement and refine methods in areas like inpainting, vSLAM, and depth estimation

• Collaborate with cross-functional teams to ensure algorithms align with business goals and product requirements

• Stay abreast of the latest trends and research in 3D AI technology, including advancements in machine learning and computer vision