LinkedIn is seeking a Staff Software Engineer to join the AI Platform team in Mountain View, California. This hybrid role will involve building and scaling LinkedIn’s AI model training, feature engineering, and model serving infrastructure. You will design high-performance data I/O, work with open-source libraries (e.g., PyTorch, HuggingFace), enable distributed training across large parameter models, and guide containerized pipeline orchestration. You will contribute to the Feature Engineering, Model Serving Infrastructure, and ML Ops domains, enabling low-latency, scalable AI workloads across billions of queries. Responsibilities include owning the technical strategy for broad or complex requirements, designing, implementing, and optimizing large-scale distributed training and serving for personalized recommendations and large language models, improving observability, mentoring engineers, and collaborating with the open-source community (e.g., vLLMs, PyTorch, GNNs, DeepSpeed, HuggingFace). Qualifications include a Bachelor's Degree in Computer Science or related field (or equivalent practical experience), 4+ years of experience building deep learning systems, hands-on experience with Java, C++, Python, Go, Rust, or functional languages, and experience with distributed systems, ML frameworks (PyTorch, TensorFlow, JAX/Flax), and data processing engines (Spark, Beam, Flink); familiarity with containers and orchestration systems. Preferred qualifications include advanced degrees and 7+ years of experience, experience with ML applications, LLM serving, GPU serving, and co-auth

LinkedIn is seeking a Staff Software Engineer to join the AI Platform team in Mountain View, California. This hybrid role will involve building and scaling LinkedIn’s AI model training, feature engineering, and model serving infrastructure. You will design high-performance data I/O, work with open-source libraries (e.g., PyTorch, HuggingFace), enable distributed training across large parameter models, and guide containerized pipeline orchestration. You will contribute to the Feature Engineering, Model Serving Infrastructure, and ML Ops domains, enabling low-latency, scalable AI workloads across billions of queries. Responsibilities include owning the technical strategy for broad or complex requirements, designing, implementing, and optimizing large-scale distributed training and serving for personalized recommendations and large language models, improving observability, mentoring engineers, and collaborating with the open-source community (e.g., vLLMs, PyTorch, GNNs, DeepSpeed, HuggingFace). Qualifications include a Bachelor's Degree in Computer Science or related field (or equivalent practical experience), 4+ years of experience building deep learning systems, hands-on experience with Java, C++, Python, Go, Rust, or functional languages, and experience with distributed systems, ML frameworks (PyTorch, TensorFlow, JAX/Flax), and data processing engines (Spark, Beam, Flink); familiarity with containers and orchestration systems. Preferred qualifications include advanced degrees and 7+ years of experience, experience with ML applications, LLM serving, GPU serving, and co-auth