לוח משרות דרושים Senior Python Developer

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

Our team is committed to developing advanced methodologies tailored specifically for multilingual environments. We focus on pre-training multilingual models, enhancing the quality of multilingual instruction-tuning datasets, refining multilingual evaluation processes, boosting knowledge transfer across languages, and optimizing multilingual tokenization among other initiatives.

The Technology & Society organization connects research, people, and ideas across Google and Alphabet to help shape and advance our most ambitious technology innovations and initiatives and their impact on users and society for the better, and responsibly. In addition, we also aim to share perspectives, engage, and collaborate with others externally on technology related issues and opportunities for society.

Responsibilities
Author research papers to share and generate impact of research results across the team and in the research community.
Research and develop technology for improving multilingual Large Language Models (LLM) such as instruction-tuning, pre-training, multilingual reasoning.
Research and develop technology for pre-training LLMs for target languages other than English.
Collaborate with other research teams to expand multilingual LLM technology.
Collaborate with Google first-party partner teams to deliver new multilingual technologies to production.

Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world.

Microsofts Azure Data engineering team is leading the transformation of analytics in the world of data with products like databases, data integration, big data analytics, messaging & real-time analytics, and business intelligence. The products our portfolio include Microsoft Fabric, Azure SQL DB, Azure Cosmos DB, Azure PostgreSQL, Azure Data Factory, Azure Synapse Analytics, Azure Service Bus, Azure Event Grid, and Power BI. Our mission is to build the data platform for the age of AI, powering a new class of data-first applications and driving a data culture.

Within Azure Data, the data integration team builds data gravity onthe Microsoft Cloud. Massive volumes of data are generated not just from transactional systems of record, but also from the world around us. Our data integration products Azure Data Factory and Power Query make it easy for customers to bring in, clean, shape, and join data, to extract intelligence.

The dataflows platform integrates many systems and products into a single unified product, empowering data, and business professionals to perform transformations and manipulations on their data ("Extract-Transform-Load").

We operate on a huge scale. We have millions of Dataflow invocations per day and dozens of thousands of monthly active users who edit or create Dataflows.

Responsibilities
Lead features end to end from design, through design, implementation to delivery and monitoring and supporting in production.
Improve and enable the engineering culture by learning and identifying areas where the team lacks knowledge or needs to improve, become subject matter expert, and leverage this knowledge in our work.
Lead the definition of requirements, KPIs, priorities, and planning of engineering deliverables.
Maintain our existing services and support them.
Embody our culture and values