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Software Engineer III, ML Infrastructure, Google Cloud Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. Minimum qualifications: Bachelors degree or equivalent practical experience.2 years of experience with software development in one or more programming languages (e.g., Python, C++ or Java).Experience in machine learning infrastructure.Experience in distributed machine learning.Experience with distributed computing and Cloud APIs. Preferred qualifications: Experience with large-scale distributed machine learning, ML performance optimizations, etc.Experience with ML frameworks (e.g., PyTorch).Experience working on Cloud and ML products.Experience in back-end system development and familiarity with common Google Infrastructure.Experience with OSS AI/ML Job scheduling software. 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. Vertex Training offers data scientists and machine learning engineers managed services to develop, train, and tune machine learning models. This flexible platform opens access to hardware and frameworks for developing machine learning models, without requiring users to manage the underlying resources. Vertex Training assists data scientists in running and monitoring their machine learning workloads, while still giving them the flexibility to bring on any type of model or code they need. The teams mission is to build a Machine Learning (ML) Training platform that empowers customers to develop scalable and performant ML models.The Google Cloud AI Research team addresses AI challenges motivated by Google Clouds mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Responsibilities Build components to enable large-scale distributed training with focus on usability, performance and resiliency.Collaborate with partner teams, dependencies (e.g., Google Compute Engine (GCE), Google Kubernetes Engine (GKE), Cloud Tensor Processing Unit (Cloud TPU), Core Infra, CoreML) to develop pre-training/post-training software to enable customer run reliable and performant workloads.Work with Product Team, Account teams and customers to identify key pain points, define scope of the problems, translate them into projects and lead them to execution.Contribute to product excellence, and improve overall product quality and user experience. Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google's Applicant and Candidate Privacy Policy. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire. If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form. Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. To all recruitment agencies: Google does not accept agency resumes. Please do not forward .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.