Software Engineer, AI Infrastructure, Cloud ML

As a software engineer at Google Cloud, you will play a crucial role in developing the next-generation technologies that revolutionize how billions of users connect, explore, and interact with information and each other. We are seeking individuals who can bring innovative ideas from various domains such as information retrieval, distributed computing, system design, networking, security, artificial intelligence, natural language processing, UI design, and mobile applications. Our team values versatility, leadership qualities, and enthusiasm to tackle new challenges across the full stack. Minimum Qualifications: - Bachelor's degree or equivalent practical experience. - 5 years of software development experience in one or more programming languages. - 3 years of experience in ML infrastructure including model deployment, evaluation, optimization, data processing, and debugging. - 3 years of experience in testing, maintaining, or launching software products. - 3 years of experience in Speech/audio technology, reinforcement learning, ML infrastructure, or specialization in an ML-related field. Preferred Qualifications: - Master's degree in Computer Science or related field, or equivalent practical experience. - 10 years of experience creating distributed systems, tools, or services in the software industry. - Experience with Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), or HPC clusters. - Familiarity with orchestration tools like SLURM or Kubernetes. - Knowledge of GPUs and related ecosystems and technologies. About the Job: As a member of the Google Cloud AI Research team, you will tackle AI challenges that align with Google Cloud's mission of democratizing AI across various industries such as tech, healthcare, finance, and retail. You will collaborate on unique research problems and strive to push the boundaries of AI by sharing your findings with the broader research community and driving real-world impact for our customers. Responsibilities: - Deploy cluster environments and provide insights on orchestrators like SLURM or Google Kubernetes Engine (GKE) to support AI/ML or HPC customers. - Design and develop tools and services with incremental milestones to support the introduction of new models to the market. - Collaborate with peers across Cloud ML Compute Services (CMCS) to influence decision-making and contribute to leadership. - Manage relationships with partners within CMCS and other teams to ensure alignment of technical designs across the organization. - Contribute to the open-source community, manage repositories, and foster internal and external community engagement around the offerings.,