Job description
Engage directly with key partners to understand, design, and implement complex inferencing capabilities for state-of-the-art deep learning models, driving innovations in AI infrastructure. Design and build robust, extensible and reusable frameworks that can support experimentation as well as production use-cases seamlessly. Work with cutting-edge hardware and software stacks to deliver best-in-class inference performance while optimizing for cost, leveraging open-source projects to advance deep learning applications. Collaborate with external and internal teams to identify new areas for improvement and contribute to innovations that enhance model performance and deployment. Discover/solve impactful technical problems, advance state-of-the-art technologies, and translate ideas into production. Developing internal tools to support the AI lifecycle, including experiment tracking, model versioning, and performance monitoring. Create deep connections within our communities, focus on increasing representation, retaining, and growing our current team members, while fostering awareness and growth through an inclusive environment. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 2+ years of experience working with deep learning frameworks (e.g., PyTorch, OnnxRuntime, Tensorflow, vLLM, TensorRT-LLM). Experience in end-to-end system design and development, with some familiarity with MLOps. Experience with low-level GPU architecture, optimizations, kernel programming, model quantization. Knowledge and experience with Docker, Kubernetes, High-performance application development. Technical leadership skills and ability to mentor early-in-profession engineers.