Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 2 years of experience in software development.
  
• Experience in Machine Learning or Artificial Intelligence.

Preferred qualifications:

• Experience in building tools to support data-driven experimentation and accelerate product development.
• Experience in developing products that handle sensitive PII data.
• Familiarity with TensorFlow, TFX, and Google/Cloud tools and systems.
• Proficiency with building large-scale ML systems.

About the job

Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

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 closely with Quality team members and partners across Google to develop robust ML infrastructure that enables faster training and removes bottlenecks.
• Develop state-of-the-art ML experimentation technology that operates within the constrained data access environment of the financial services industry, delivering to Google engineers and customers accurate performance and latency telemetry.
• Deliver ML pipeline automation with chaining, queueing, and resource management for Pipeline operations.
• Enable the Quality team to manage the complete experimentation life cycle for ML pipeline runs, tracking their status and providing status management and access tools for collected metrics.