Rebellions | 리벨리온대한민국 경기도 성남시 분당구 정자일로 248, 18층 리벨리온
Responsibilities and Opportunities
Act as the primary technical contact for customers throughout the engagement lifecycle, delivering exceptional guidance and ongoing support
Accelerate the adoption of Rebellions’ AI solutions by creating and delivering tailored product demos and technical presentations for real-world AI applications (e.g., LLM inference, vision pipelines) and serving frameworks (e.g., vLLM)
Develop a deep understanding of Rebellions’ AI solutions by evaluating end-to-end performance (e.g., throughput, latency, energy efficiency) and producing insightful reports
Create clear, concise technical documentation, best practices, and integration guides for customers and partners
Collaborate closely with internal software and hardware engineering teams to provide customer feedback and influence future product improvements and business opportunities
Key Qualifications
Bachelor’s degree in Computer Science, Electrical Engineering, or a related technical field
Strong problem-solving skills with a proactive, analytical approach to technical challenges
Solid understanding of AI inference and serving frameworks such as PyTorch, vLLM, or TensorRT
Hands-on experience with Python for inference workloads, benchmarking, and debugging
Excellent communication and collaboration skills; experience working with internal cross-functional teams and directly engaging with customers
Ideal Qualifications
Hands-on experience designing and deploying end-to-end AI systems, with a focus on inference, serving, system-level optimization, and performance benchmarking
Knowledge of hardware acceleration (NPU, GPU, edge AI chips) and practical experience with model optimization techniques (e.g., quantization, pipelining)
Familiarity with modern AI model architectures, including LLMs, CNNs, and transformers
Prior experience in technical customer-facing roles such as Field Application Engineer (FAE), Solutions Engineer, or Sales Engineer
Experience writing technical content such as blogs, documentation, or whitepapers targeting AI/ML practitioners
Act as the primary technical contact for customers throughout the engagement lifecycle, delivering exceptional guidance and ongoing support
Accelerate the adoption of Rebellions’ AI solutions by creating and delivering tailored product demos and technical presentations for real-world AI applications (e.g., LLM inference, vision pipelines) and serving frameworks (e.g., vLLM)
Develop a deep understanding of Rebellions’ AI solutions by evaluating end-to-end performance (e.g., throughput, latency, energy efficiency) and producing insightful reports
Create clear, concise technical documentation, best practices, and integration guides for customers and partners
Collaborate closely with internal software and hardware engineering teams to provide customer feedback and influence future product improvements and business opportunities
Key Qualifications
Bachelor’s degree in Computer Science, Electrical Engineering, or a related technical field
Strong problem-solving skills with a proactive, analytical approach to technical challenges
Solid understanding of AI inference and serving frameworks such as PyTorch, vLLM, or TensorRT
Hands-on experience with Python for inference workloads, benchmarking, and debugging
Excellent communication and collaboration skills; experience working with internal cross-functional teams and directly engaging with customers
Ideal Qualifications
Hands-on experience designing and deploying end-to-end AI systems, with a focus on inference, serving, system-level optimization, and performance benchmarking
Knowledge of hardware acceleration (NPU, GPU, edge AI chips) and practical experience with model optimization techniques (e.g., quantization, pipelining)
Familiarity with modern AI model architectures, including LLMs, CNNs, and transformers
Prior experience in technical customer-facing roles such as Field Application Engineer (FAE), Solutions Engineer, or Sales Engineer
Experience writing technical content such as blogs, documentation, or whitepapers targeting AI/ML practitioners