Conduct cutting-edge research on high-performance embodied AI systems, with a focus on robotic manipulation and autonomous decision-making.
Responsibilities
Develop novel reinforcement learning, imitation learning, and learning-from-demonstration algorithms to improve the safety, robustness, and generalization of embodied agents
Design and evaluate methods for leveraging negative data, failure cases, human feedback, and corrective demonstrations to improve policy learning
Develop and study Vision-Language-Action (VLA) models, Vision-Language Models (VLMs), and embodied AI agents for embodied intelligence and robot learning applications
Implement, train, and evaluate state-of-the-art models in both simulation and real-world robotic environments
Collaborate closely with researchers and engineers to translate research ideas into scalable systems and internal technology prototypes
The Conversational AI Engineer (CAI) is responsible for developing and deploying Conversational AI solutions: chatbots and voicebots, using LLMs, Agentic AI, and NLU models. This role manages technical implementations and integrations across environments, ensuring compliance with standards and best practices.
Responsibilities
Implement system integrations for seamless automation across platforms
Engineer prompts and structure knowledge bases to ensure accurate and context-aware responses
Ensure safe and reliable AI model by implementing safeguards and governance measures
Configure and maintain STT/TTS systems, ensuring high-quality voice performance and seamless telephony integration
Document architectures, configurations, and deployment processes to maintain transparency and reproducibility
AI Engineer specializing in Neural Scene Reconstruction for autonomous driving simulation environments.
Responsibilities
Develop and improve neural scene reconstruction models using 3DGS, NeRF, and other learned 3D representations to reconstruct realistic driving environments.
Implement dynamic scene reconstruction techniques, modeling static environments and moving traffic agents while handling occlusions, incomplete observations, and challenging environmental conditions.
Develop neural rendering and novel-view synthesis methods to generate realistic camera observations from reconstructed 3D scenes.
Contribute to multimodal sensor rendering, including camera, LiDAR, and radar simulation, to support realistic virtual testing environments.
Build scalable reconstruction and rendering pipelines for efficient experimentation and large-scale processing of real-world driving datasets.
Requirements
Bachelor's or Master's degree in Computer Science, Computer Vision, Robotics, Electrical Engineering, or a related discipline, or equivalent practical experience.
Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Applies GenAI models as part of the solution, including deep learning, neural networks, chatbots, image processing.
Responsibilities
Lead the development and deployment of AI models ensuring scalability and reliability.
Mentor junior team members to foster skill development and knowledge sharing.
Coordinate with stakeholders to align AI initiatives with organizational objectives.
Provide solutions to problems for their immediate team and across multiple teams.
Engage with multiple teams and contribute on key decisions.
Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution.
Responsibilities
Expected to perform independently and become an SME.
Required active participation/contribution in team discussions.
Contribute in providing solutions to work related problems.
Collaborate with stakeholders to understand requirements and translate them into technical solutions.
Continuously update knowledge on emerging AI technologies and integrate relevant advancements into projects.
As an AI / ML Engineer, a typical day involves designing and building advanced applications and systems that integrate artificial intelligence technologies. This role requires managing both cloud-based and on-premises application pipelines to ensure solutions are production-ready and reliable. The work often includes implementing generative AI models and exploring various AI domains such as deep learning, neural networks, conversational agents, and image analysis.
Responsibilities
Perform independently and become an SME
Active participation/contribution in team discussions
Contribute in providing solutions to work related problems
Collaborate with team members to identify opportunities for AI integration and improvement
Assist in documenting technical processes and sharing knowledge with junior colleagues
Lead AI research and technical strategy for medical imaging and Physical AI systems, driving end-to-end development from concept to clinical deployment.
Responsibilities
Define and own long-term research vision for medical image analysis and Physical AI-enabled imaging systems
Architect and lead development of state-of-the-art detection, segmentation, classification, reconstruction, and quantitative imaging systems
Drive innovation in vision and multimodal transformers, large-scale foundation models, self-supervised learning, physics-informed ML, and simulation-to-real transfer
Establish technical standards for experimental rigor, physical robustness, uncertainty quantification, clinical validation, fairness, and generalization
Partner with clinicians, radiologists, pathologists, imaging scientists, and regulatory stakeholders to formulate AI problem statements
Requirements
PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Applied Physics, Robotics, or a closely related field
Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution.
Responsibilities
Expected to be an SME, collaborate and manage the team to perform.
Responsible for team decisions.
Engage with multiple teams and contribute on key decisions.
Provide solutions to problems for their immediate team and across multiple teams.
Lead the development and deployment of AI models ensuring scalability and reliability.
As an AI / ML Engineer, a typical day involves designing and building advanced applications and systems that leverage artificial intelligence technologies and cloud-based AI services. The role requires ensuring that these solutions are production-ready, whether deployed on cloud platforms or on-premises. The engineer integrates generative artificial intelligence models into various projects, which may encompass deep learning techniques, neural network architectures, conversational agents, and image processing tasks.
Responsibilities
Be an SME, collaborate and manage the team to perform
Responsible for team decisions
Engage with multiple teams and contribute on key decisions
Provide solutions to problems for their immediate team and across multiple teams
Lead the development and deployment of AI models ensuring scalability and reliability
Plan and perform rapid prototyping of computer vision and multimodal machine learning techniques applied to determining authenticity of media information.
Design and train multimodal models capable of complex visual reasoning.
Undertake exploratory analysis to inform experimentation and research directions.
Engage with product teams to drive the development of our research.
Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently.
Researcher - Embodied AINewHuawei Technologies Canada Co., Ltd. · Edmonton, Canada
Conversational AI EngineerNewFoundever® · Remote, India
Senior Research Engineer, Multimodal Reasoning and Media ForensicsGoogle · Mountain View, US+1 more