Technical Lead Manager, AI Flow Platform, GDM Deployment

1 Month ago • 8-13 Years • Artificial Intelligence • $197,000 PA - $291,000 PA

Job Summary

Job Description

This Technical Lead Manager role focuses on building and deploying Google's GenAI development platform. Responsibilities include leading a team of engineers in building production-ready infrastructure for GenAI features, collaborating with cross-product teams (Search, Ads, YouTube), designing and implementing customer-requested GenAI features (using C++ and Python), driving strategy for incorporating emerging GenAI technologies, and coaching the team to high efficiency. The role requires expertise in ML infrastructure, applied ML/AI algorithms, LLMs, NLP, and modern ML frameworks (JAX, PyTorch, TensorFlow), along with strong technical and people management skills. The platform aims to seamlessly integrate GenAI models into Google's software development lifecycle, supporting strong partnerships across product areas.
Must have:
  • 8+ years software development experience
  • 5+ years ML infrastructure/AI algorithm experience
  • 3+ years technical leadership
  • 2+ years people management
  • C++ or Python proficiency
Good to have:
  • Experience with JAX, PyTorch, or TensorFlow
  • End-to-end experience launching ML/NLP projects
  • Frontend coding (AngularJS) experience

Job Details

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development, and with data structures/algorithms.
  • 5 years of experience with ML infrastructure, applied ML/AI algorithms and tools, LLMs or other multimodal foundation models, and natural language processing and working with C++ or Python.
  • 3 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.

Preferred qualifications:

  • Experience with modern ML frameworks (e.g., JAX, Pytorch or TensorFlow).
  • End-to-end experience with launching applied ML/NLP projects.
  • End-to-end experience with FrontEnd coding/Angular JS.
  • Ability to work effectively in a fast-moving environment with a high degree of ambiguity.

About the job

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

We are seeking highly motivated software engineer to build the development of our GenAI development platform. You'll play a pivotal role in ensuring that Google products are able to seamlessly incorporate latest GenAI models into their software development lifecycle.

We're building a unified toolchain that helps Google product teams build and launch GenAI-powered products. We have users across Alphabet and collaborate deeply with products such as Search, Ads, Youtube, etc. We enjoy close partnerships with Google DeepMind and core infrastructure teams across Google, and through the unique proximity to GDM we collaborate closely with researchers building the next generation of GenAI.

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.

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 .

Responsibilities

  • Build big bets into the AI Flow platform that incorporates the entire spectrum of Classic ML to state-of-the-art GenAI into the Google product development lifecycle.
  • Lead a team of highly-motivated engineers to land strategic tracks in production-ready infra with high inflection points and high impact, supporting strong partnerships cross-product area.
  • Design and implement customer-requested GenAI features through multiple stages. Develop and maintain our products, written in C++ and Python, with Google-wide coding, testing, production standards.
  • Drive strategy and collaborate with stakeholders to identify emerging technology related to GenAI and develop a plan for translating these into practical solutions for Google products.
  • Coach the team into high efficiency, fast pivots, while landing production-ready code. Promote a culture of continuous learning and improvement, and actively grow the team members into their next best selves.

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