安克创新Assistant Algorithm Engineer
任职要求
Master’s degree or above in Computer Science, Automation, Electronic Information, Mathematics, or related fields. Strong communication skills, with a collaborative team-oriented mindset. Highly goal-driven, responsible, and capable of solving problems effectively. Familiarity with deep learning frameworks such as TensorFlow or PyTorch is preferred. Excellent English proficiency will be considered a strong advantage.
工作职责
Participate in the design and development of AI-based computer vision algorithm products, assisting in solving technical challenges during project implementation. Responsible for training, testing, and optimising algorithm models to improve performance and stability. Stay up-to-date with cutting-edge computer vision algorithm technologies and explore their innovative applications in products.
Why Join Us We're seeking an AI Algorithm Engineer who will lead the design and implementation of cutting-edge conversational systems for TCL products such as TV, mobile phones, smart glasses and home robots. This position is ideal for someone who is motivated to innovate, tackle challenging problems, and collaborate effectively in cross-functional teams. Key Responsibilities Lead the algorithm design and data flow architecture for specific scenarios. Adapt LLMs to specific application scenarios via continued pre-training or fine-tuning. Evaluate and monitor model performance using both quantitative and qualitative metrics; recommend the best models for specific tasks. Stay current with AIGC trends and apply cutting-edge technologies and methods to work. Solve complex problems, share knowledge, and collaborate across departments.
The objective of this role is to elevate Apple’s voice assistant and search to a new level of intelligence and accuracy through the application of advanced techniques. This encompasses various areas such as enhancing data management, optimizing pipeline processes, improving services, and refining modeling algorithms. Additionally, the team collaborates closely with other engineers to swiftly develop experiments and implement prototypes. By consistently tackling novel challenges, they strive to create a remarkable product that prioritizes accuracy, usability, and optimal performance. This unique opportunity at the forefront of machine learning and software engineering combines a diverse set of skills and innovation. Your work will have a profound impact on hundreds of millions of users worldwide.
About the Role TCL Industries seeks a visionary Embodied AI Architect to lead software architecture for next-generation home assistant robots. You will design intelligent, interactive systems, integrating perception, planning, navigation, manipulation, HRI, and emotional modules. Collaborate with multidisciplinary teams to drive robotics innovation. Key Responsibilities •System Architecture: •Design and oversee end-to-end software architecture for embodied AI in home robots. •Define interfaces and protocols for system components. •Manage sensor and camera data for informed AI decision-making. •Guide middleware, simulation, and tool selection. •Integration & Optimization: •Ensure seamless integration across hardware, AI algorithms, and cloud services. •Profile, debug, and optimize system performance. •Establish best practices for development, testing, and deployment. •Collaboration: •Work with hardware, data science, and product teams to align technical and product goals. •Cooperate with researchers to design and develop advanced algorithms for perception, reasoning, planning, human-robot interaction (HRI) and emotional intelligence •Strategic Input: •Contribute to long-term vision and planning. •Represent the lab at conferences and industry events.
- Keep up to date with and utilize the latest developments in LLM system optimization.- Take the lead in designing innovative system optimization solutions for internal LLM workloads.- Optimize LLM inference workloads through innovative kernel, algorithm, scheduling, and parallelization technologies.- Continuously develop and maintain internal LLM inference infrastructure.- Discover new LLM system optimization needs and innovations.