Tech Lead, Machine Learning, Google Chat

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 3 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

Preferred qualifications:

• 2 years of experience in machine learning engineering with a proven track record of successful project delivery.
• Experience with the entire machine learning development lifecycle, including data preprocessing, model training, evaluation, and deployment.
• Experience in full-stack development, including proficiency in programming languages like Python, Java, web frameworks, and cloud platforms.
• Knowledge of machine learning algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, and generative AI.
• Excellent communication, collaboration, and problem-solving skills.
• Passion for innovation and a drive to push the boundaries of machine learning technology.

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 Chat is an intelligent communication app and messaging platform. As a key component of Google Workspace, it is revolutionizing the way people communicate and collaborate with each other.

Chat had a breakout year in 2023-2024 with strong product growth, improvements in core architecture and fundamentals, and a major marketing moment at Cloud Next. In 2025, we built on that momentum with deep investments in AI. Chat is one of the growing products in Workspace and we aim to make it the most logical choice for any Workspace customer that needs a real time communication/collaboration app!

This year will be about differentiation with AI on product excellence and performance.

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.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Design and implement, scalable, and production-ready machine learning pipelines.
• Stay current with the latest advancements in machine learning research and translate them into practical solutions for real-world business problems.
• Mentor and guide junior engineers on best practices in machine learning engineering and full-stack development.
• Participate in the development and improvement of the team's architecture for AI features, especially on the data/eval side.
• Lead the full lifecycle of machine learning projects, from problem definition and data acquisition to model development, deployment, and monitoring.