AI Customer Engineer III, Life Sciences, Google Cloud
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In-office locations: Austin, TX, USA; Atlanta, GA, USA; New York, NY, USA.
Remote location(s): United States.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience with cloud native architecture in a customer-facing or support role.
- Experience in conducting technical workshops, proof-of-concepts, and customer evaluations with executive leaders and stakeholders in the life sciences or pharmaceutical industries.
- Experience with Artificial Intelligence (AI) Infrastructure (e.g., GPUs/TPUs), architectures, and technologies.
Preferred qualifications:
- Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
- Experience with Graphics Processing Unit/Tensor Processing Unit (GPU/TPU) computing and its application in scientific field on cloud platforms.
- Experience with cloud services related to AI, data analytics, and scientific computing.
- Knowledge of scientific workflows and software used in drug discovery (e.g., molecular modeling, simulations, cheminformatics).
- Knowledge of industry compliance and security requirements related to Life Sciences (e.g., HIPAA, GxP).
About the job
When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.In this role, you will partner with technical business teams as a subject matter expert in the Life Sciences and Pharmaceutical industry, with a specialization in Drug Discovery and AI Infrastructure (GPU/TPU) workloads. You will be the technical expert working alongside our business teams to engage with leading Life Sciences organizations, helping them leverage the power of Google Cloud to accelerate their research, development, and innovation in drug discovery pipelines. You will be responsible for understanding the technical and scientific tests faced by our customers in drug discovery, and then architect and demonstrate Google Cloud solutions that address their needs. You will involve analysis of their AI/High Performance Computing infrastructure, scientific workflows, data analytics pipelines, and cloud adoption strategies.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Work with the team to identify and qualify business opportunities, understand customer technical objections, and develop the strategy to resolve technical blockers in the context of drug discovery research and development.
- Recommend and document migration paths, integration strategies, enterprise architectures, platforms, and application infrastructure required to implement a complete solution on Google Cloud.
- Guide workshops for customers to assess existing application/infrastructure environments and organizational readiness. Identify use cases for cloud computing and paths to digital transformation.
- Develop architectural blueprints to enable infrastructure, high performance computing, and mainframe migration opportunities, coordinating with Product and Specialist teams.
- Articulate the value proposition of Google Cloud for Life Sciences and High Performance Computing (HPC), demonstrating how Google Cloud Platform (GCP) solutions can accelerate drug discovery timelines and improve scientific outcomes.
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