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Our computational challenges are so big, complex and unique we can't just purchase off-the-shelf hardware, we've got to make it ourselves. Your team designs and builds the hardware, software and networking technologies that power all of Google's services. As a Hardware Engineer, you design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You develop from the lowest levels of circuit design to large system design and see those systems all the way through to high volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers affecting millions of Google users.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Responsibilities
Collaborate with Design, Architecture, Process Technology, and Silicon Engineering teams to develop new product bring-up, verification, characterization, qualification strategies and manufacturing test solutions for new High Performance Computing (HPC) products in advanced manufacturing technologies.
Verify test solutions on pre-silicon models (simulation or emulation).
Develop and validate test programs on Automated Test Equipment (ATE) platforms in preparation for High Volume Manufacturing, working with ATE Vendor and internal cross-functional teams.
Support product sustain, including volume data analytics, test time and yield improvements, assess test escapees and Return Merchandise Authorization (RMA) returns, localize failures, implement containment measures in the manufacturing test flow, and partner with manufacturing, quality and reliability teams to implement root cause corrective actions.

Our computational challenges are so big, complex and unique we can't just purchase off-the-shelf hardware, we've got to make it ourselves. Your team designs and builds the hardware, software and networking technologies that power all of Google's services. As a Hardware Engineer, you design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You develop from the lowest levels of circuit design to large system design and see those systems all the way through to high volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers affecting millions of Google users.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Responsibilities
Collaborate with Design, Architecture, Process Technology, and Silicon Engineering teams to develop characterization and manufacturing test solutions for new High Performance Computing (HPC) products in advanced manufacturing technologies.
Validate test solutions on system-level platforms and prepare for mass production.
Work with hardware and software teams to evaluate functional device yield and performance across various operating conditions, characterize hardware/software interaction, and develop effective production screens to reduce Defective Parts per Million (DPPM).
Assess test escapees and RMA returns, localize failures, implement containment measures in the manufacturing test flow, and partner with manufacturing, quality, and reliability teams to implement root cause corrective actions.

Our Tel-Aviv team develops machine-learning models that serve billions of buyers daily. To do so, we develop state-of-the-art recommendation and ranking systems. We work closely with domain experts to design features and embedding spaces best fitted for eCommerce. Finally, we use Meta's cutting-edge generative models to personalize the experience for our buyers.
Our team also builds AI capabilities to enhance Metas product catalogs to improve ads performance. Catalog-powered ads account for a significant portion of Metas revenue, with unlocking deep semantic understanding of our products being a key strategic lever for revenue growth.
If you're passionate about ML at scale and want to work with an amazing group of people - look no further - come and join us!
Software Engineer, Machine Learning Responsibilities
Leading projects or small teams of people to help them unblock, advocating for ML excellence
Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
Suggest, collect and synthesize requirements and create effective feature roadmaps
Code deliverables in tandem with the engineering team

Our mission is to pioneer advanced Feature and ML Platforms to accelerate the Financial Integrity org, by providing an uncompromised top industry risk, compliance and customer care experiences

AI is one of Metas leading investment areas as part of the financial activity expansion in our portfolio of products. As part of this, we are building best-in-class infrastructure using best-in-class ML technologies

The team is composed of software engineers, machine learning engineers, data scientists in developing state of the art backend systems and ML Infrastructure and our focus is to continue to lead industry standards in the FinTech space.
Software Engineer, Infrastructure Responsibilities
Lead complex technical efforts across many engineers
Design core, backend software components
Code using primarily C/C++, Java, PHP and Hack
Interface with other teams to incorporate their innovations and vice versa
Conduct design and code reviews
Analyze and improve efficiency, scalability, and stability of various system resources
Set direction and goals for the team regarding project impact, product quality and engineering efficiency
Lead major initiatives, projects, teams, roll-outs and phased-releases
Helps onboard new team members, provides mentorship and enables successful ramp up on your team's code bases
Proactively identify and drive changes as needed for assigned codebase, product area and/or systems

We are looking for researchers and applied scientists to join the Central Applied Science team. Central Applied Science is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally contribute to Meta's success. By applying your expertise in quantitative methods, you will be empowered to drive impact across a range of products, infrastructure and company operations. Individuals in this role are expected to have expertise and publications within research areas including artificial intelligence, machine learning, statistics, causal inference and experimentation. The ideal candidate will have a passion for building data-driven products and forming research frameworks to solve challenging, real-world problems.
Research Scientist, Central Applied Science (PhD) Responsibilities
Work with vast amounts of data, generate research questions that push the state-of-the-art, and build data-driven products.
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure.
Learn new tools, systems, and programming languages quickly as required by the particular project you are working on
Work towards long-term ambitious research goals, while identifying intermediate milestones.
Communicate best practices in quantitative analysis to partners.
Work both independently and collaboratively with other scientists, engineers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value.
Publish research results in top-tier conferneces and at leading journals
Actively identify new opportunities within Meta's long term roadmap for data science contributions.

In Shop Ads, we personalize sellers' shops to delight our buyers' and satisfy their needs. From the products to the text and media that complement them, the layout that makes it most accessible - every decision has the buyer in mind.
Our Tel-Aviv team develops machine-learning models that serve billions of buyers daily. To do so, we develop state-of-the-art recommendation and ranking systems. We work closely with domain experts to design features and embedding spaces best fitted for eCommerce. Finally, we use Meta's cutting-edge generative models to personalize the experience for our buyers.
Our team also builds AI capabilities to enhance Metas product catalogs to improve ads performance. Catalog-powered ads account for a significant portion of Metas revenue, with unlocking deep semantic understanding of our products being a key strategic lever for revenue growth.
If you're passionate about ML at scale and want to work with an amazing group of people - look no further - come and join us!
Software Engineer, Machine Learning Responsibilities
Play a role in setting the direction and goals for the ML pillar, in terms of project impact, ML system design, and ML excellence.
Owning the entire development cycle, from ideation to realization across the stack. Designing and developing machine learning models using supervised, unsupervised, and deep learning techniques.
Implementing and testing machine learning models in a production environment
Take part in the overall team engineering efforts and contribute with hands-on work
Working with software engineers to integrate machine learning models into applications and systems
Collaborating with data scientists to identify and select appropriate machine learning algorithms and techniques