About the Role: 

We are seeking a highly motivated AI R&D Engineer to join our cutting-edge research and development team. In this role, you will be responsible for exploring, developing, and implementing novel AI algorithms and systems that push the boundaries of what’s possible. You’ll work at the intersection of research and engineering, turning theoretical models into production-ready solutions. 

Key Responsibilities: 

• Conduct research in machine learning, deep learning, computer vision, natural language processing, or other AI domains. 

• Design, implement, and optimize innovative AI algorithms and models. 

• Analyze experimental results, iterate quickly, and improve performance. 

• Collaborate with cross-functional teams including data scientists, software engineers, and product managers. 

• Translate research findings into scalable, production-level code. 

• Stay current with the latest AI research, tools, and technologies, and evaluate their applicability. 

• Publish and present research results in academic or industry venues, when appropriate. 

Qualifications: 

• Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s or Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, Mathematics, or a closely related discipline is highly desirable. 

• 5 to 7+ years of professional software engineering experience, with a strong emphasis on AI/ML system development. 

• In-depth knowledge of machine learning, statistical modeling, and data science concepts. 

• Proficiency in Python and hands-on experience with leading ML frameworks such as PyTorch, TensorFlow, or JAX. 

• Proven ability to work with large-scale datasets and conduct scientific computing in high-performance or distributed environments. 

• Strong foundation in algorithm design, computational complexity, and modern software engineering practices. 

• Excellent analytical and problem-solving abilities, with a creative and critical-thinking mindset. 

• A record of published research in top-tier AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ACL). 

• Practical experience in deploying machine learning models into production and maintaining them at scale. 

• Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization technologies like Docker and Kubernetes. 

• Familiarity with advanced AI techniques such as reinforcement learning, generative models, or foundation models (e.g., LLMs, diffusion models). 

Soft Skills: 

• Analytical Problem-Solving: Strong debugging skills with a history of resolving complex technical challenges efficiently. 

• Effective Communication: Capable of explaining technical concepts clearly to both technical stakeholders and non-technical audiences. 

• Adaptability: Thrives in dynamic, fast-paced environments and adjusts quickly to evolving priorities and requirements. 

• Sound Decision-Making: Confident in evaluating trade-offs and making decisions that balance technical debt, long-term maintainability, and delivery speed.