Senior Software Engineer - AI Platform at LinkedIn. Location: Mountain View, California, United States. Work arrangement: Hybrid (work from home and LinkedIn office on select days). You will be part of the AI Platform team responsible for scaling LinkedIn's AI model training, feature engineering, and serving across billions of parameters. The role involves building high-performance training infrastructure, optimizing distributed training, enabling GPU-accelerated inference, improving observability, and collaborating with open-source projects (e.g., PyTorch, HuggingFace, vLLMs, DeepSpeed). You will work on Model Training Infrastructure, Feature Engineering, Model Serving Infrastructure, and ML Ops within a scalable platform that supports large-scale recommendations and language models. Responsibilities include designing and optimizing large-scale distributed serving/training, improving developer productivity, mentoring engineers, and collaborating with internal AI teams and the open-source community. Qualifications: Bachelor’s Degree in Computer Science or related technical discipline, or equivalent practical experience; 2+ years of experience building deep learning systems; 2+ years of experience with Java, C++, Python, Go, Rust, C# and/or Scala; hands-on experience developing distributed systems; familiarity with containers and container orchestration; experience with deep learning frameworks such as PyTorch, TensorFlow, JAX/Flax; experience with distributed data processing engines like Spark, Flink, Beam; knowledge of ML tooling like MLFlow, Kubeflow is a plus. Preferred

Senior Software Engineer - AI Platform at LinkedIn. Location: Mountain View, California, United States. Work arrangement: Hybrid (work from home and LinkedIn office on select days). You will be part of the AI Platform team responsible for scaling LinkedIn's AI model training, feature engineering, and serving across billions of parameters. The role involves building high-performance training infrastructure, optimizing distributed training, enabling GPU-accelerated inference, improving observability, and collaborating with open-source projects (e.g., PyTorch, HuggingFace, vLLMs, DeepSpeed). You will work on Model Training Infrastructure, Feature Engineering, Model Serving Infrastructure, and ML Ops within a scalable platform that supports large-scale recommendations and language models. Responsibilities include designing and optimizing large-scale distributed serving/training, improving developer productivity, mentoring engineers, and collaborating with internal AI teams and the open-source community. Qualifications: Bachelor’s Degree in Computer Science or related technical discipline, or equivalent practical experience; 2+ years of experience building deep learning systems; 2+ years of experience with Java, C++, Python, Go, Rust, C# and/or Scala; hands-on experience developing distributed systems; familiarity with containers and container orchestration; experience with deep learning frameworks such as PyTorch, TensorFlow, JAX/Flax; experience with distributed data processing engines like Spark, Flink, Beam; knowledge of ML tooling like MLFlow, Kubeflow is a plus. Preferred