AI Staff Engineer

Job Descriptions:

- Participate in the design, development, and deployment of AI/ML solutions for complex enterprise problems, with a focus on NLP, Computer Vision, Generative AI, and foundation models (LLMs, LMMs, Foundation Models).
- Serve as a core technical contributor in tackling and solving challenging AI problems; optimize models to ensure performance, stability, and scalability in real-world environments.
- Participate in the evaluation and experimentation of AI technologies, models, and frameworks; propose technical solutions and optimization approaches for AI systems.
- Collaborate with product, data, and infrastructure teams to integrate and operate AI solutions across GPU, cloud, and enterprise infrastructures.
- Mentor, support, and share knowledge with team members; contribute to building and continuously updating technical documentation based on real-world implementation results.

Requirments: 
- At least 4–6 years of hands-on experience developing AI/ML/Deep Learning systems in enterprise environments or large-scale projects.
- Experience training, fine-tuning, or deploying AI models (NLP, Computer Vision, Generative AI; exposure to LLMs/Foundation Models is a strong plus) and integrating them into real-world systems or products.
- Involved in the design, implementation, and operation of training and inference pipelines; hands-on experience working with GPU, cloud, or AI computing infrastructures.
- Served as a core technical contributor on complex AI problem domains; participated in experimentation, evaluation, and recommendation of appropriate technical approaches.
- Ability to mentor, share knowledge, and provide technical support to team members; solid understanding of and adherence to data, security, and AI system quality requirements.
- Bachelor’s degree or higher in Artificial Intelligence, Machine Learning, Computer Science, Computer Engineering, Information Technology, Data Science, or related fields.
- Candidates with a Master’s degree or formal training from reputable universities, domestically or internationally, are strongly preferred.
- Professional certifications in AI/ML or platforms such as Cloud, GPU computing, or distributed systems are an advantage.
- Preference for candidates with experience participating in applied research topics, R&D projects, or AI research-to-production collaboration programs.
- Participated in real-world enterprise AI projects in key technical roles, contributing to improvements in system performance, product quality, or operational efficiency.
- Demonstrated clear contributions to the development, enhancement, or optimization of AI systems or products (NLP, Computer Vision, Generative AI; exposure to LLMs/Foundation Models is a strong plus) that have been put into practical use.
- Delivered technical solutions, improvements, or professional documentation adopted within the team or business unit; preference for candidates who have shared knowledge internally or participated in applied R&D activities.
- Involved in the development or optimization of AI pipelines on GPU and cloud infrastructures, contributing to improved training and inference performance or overall system stability.