Senior Technical Program Manager, Machine Learning, Google Cloud

Google
London
1 year ago
Applications closed

Related Jobs

View all jobs

Sr Product Manager, Data Science

Technical Program Manager - Machine Learning - New York

Technical Program Manager - Machine Learning - New York

Technical Program Manager - Machine Learning - New York

Senior Data Scientist - QuantumBlack Labs

Senior Manager, Data Science - eBay Live

Minimum qualifications: - Bachelor's degree in a relevant field, or equivalent practical experience. - 8 years of experience in program management. - Experience with machine learning/AI in a software development environment. Preferred qualifications: - 8 years of experience managing cross-functional or cross-team projects. - Experience in launching Machine Learning or Artificial Intelligence products from research to production. A problem isn't truly solved until it's solved for all. That's why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you'll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You'll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers. Our goal is to build a Google that looks like the world around us - and we want Googlers to stay and grow when they join us. As part of our efforts to build a Google for everyone, we build diversity, equity, and inclusion into our work and we aim to cultivate a sense of belonging throughout the company. In this role, you will play a key role in accelerating Gemini from research to production. You will partner with product managers, engineers, and leadership to define road maps, prioritize features, and ensure timely releases. 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. - Implement communications standards across a portfolio of programs including executive and key partner communications, including working with researchers, engineers, and other stakeholders to define and prioritize Large Language Model (LLM) features and capabilities. - Establish a reliable and visible cadence for program reviews, decision-making, prioritization, and resource stewardship (effective deployment of machine and people resources) such as efficiency and utilization gains are measurable and the impact can be felt organization wide. - Lead a governance structure that drives effective executive decision-making. Ensure governance structure effectively exposes and mitigates dependencies. - Define a program portfolio solving problems that target high business impact for the organization and product area. - Develop and manage the overall program plan for LLM development and deployment (requirements gathering, risk assessment, and resource allocation) in order to deploy LLMs in a production-ready environment, meeting user needs. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See alsohttps://careers.google.com/eeo/andhttps://careers.google.com/jobs/dist/legal/OFCCPEEOPost.pdfIf you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form:https://goo.gl/forms/aBt6Pu71i1kzpLHe2.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.