Software Engineer Ml Jobs (NOW HIRING)

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), Machine learning (ML) infrastructure, or specialization in another ML field.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

Preferred qualifications:

• Master's degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience in predictive maintenance, anomaly detection, or systems reliability engineering.
• Ability to translate complex technical findings into actionable business strategies for executive stakeholders.

About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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 take control of the world's largest data center footprint as an Applied AI/ML Specialist on a team responsible for the fault tolerance of Google's entire fleet, including the ML TPUs. You will pioneer the use of AI/ML to solve complex infrastructure challenges by leveraging petabytes of operational and telemetry data, directly empowering the very AI/ML systems that drive the future of Google.
The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities

• Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
• Design and implement AI/ML models to predict, detect, and mitigate hardware and software faults across a global fleet.
• Analyze petabytes of telemetry and performance data to uncover insights that improve the reliability of ML TPUs and traditional compute infrastructure.
• Build scalable automated systems that allow Google's data center footprint to grow while maintaining industry-leading uptime.
• Partner with hardware designers and site reliability engineers (SREs) to integrate intelligent diagnostics into the core data center lifecycle.

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