Staff Software Engineer, Machine Learning Infrastructure
Job Summary
Job Description
- 8+ years software development experience
- 5+ years ML algorithms & tools experience
- 5+ years experience launching software products
- Design, implement telemetry for ML resource monitoring
- Identify and build solutions to improve ML fleet efficiency
- Build reporting and analytics solutions
- Experience with Kubernetes, Google Kubernetes Engine
- GPU Programming, TensorFlow, and Cloud experience
- Experience analyzing ML model performance or working on LLMs
- Knowledge of CPU/GPU architecture or HW accelerators
9 skills required
Job Details
Minimum qualifications:
- Bachelor's degree in Computer Science or a related technical field or equivalent practical experience.
- 8 years of experience with software development in one or more programming languages, and with data structures/algorithms.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning or natural language processing.
Preferred qualifications:
- Experience with Kubernetes, Google Kubernetes Engine, GPU Programming, TensorFlow, and Cloud.
- Experience analyzing ML models performance or working on LLM prompting, training or developing LLMs.
- Experience and knowledge of CPU/GPU architecture or HW accelerators
- Ability to quickly adapt to new tools, frameworks, and languages.
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.
In this role, you will join a team that's part of Google's Core ML organization, focused on optimizing Google's Machine Learning resources. You will help develop monitoring tools and dashboards to track the performance and efficiency of TPUs and GPUs, which are used across all Google products. This data helps to improve resource allocation, identify areas for improvement, and drive efficiency gains across Google's products.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Design, implement and advance the telemetry capabilities needed for monitoring and evaluating the fleet-wide efficiency of ML resources (TPUs and GPUs). This includes identifying the right underlying signals, devising the right high-level metrics of interest, and creating common dashboards for highlighting fleet-wide performance and efficiency.
- Identify opportunities to improve the efficiency of the ML fleet and build solutions and capabilities to improve ML fleet efficiency.
- Build reporting and analytic solutions with key partners, and provide in-depth analysis of the metrics to improve the operation and utilization of ML resources.
- Drive collaboration with various teams (across different PAs) as needed to accomplish the efficiency improvement goals.
- Lead junior SWEs towards delivering project goals.
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