לוח משרות דרושים AI

Meta is seeking a Research Scientist to join our Llama Large Language Model (LLM) Research team. We are looking for recognized experts in VLLMs; with experience in areas like vision encoders, data filtering/curation for pre and post-training, RLHF, responsible AI and model controllability. The ideal candidate will have an interest in producing and applying new science to help us develop and responsibly release vision large language models.
AI Research Scientist - Generative AI Responsibilities
Lead, collaborate, and execute on research that pushes forward the state of the art in multimodal reasoning and generation research.
Work towards long-term ambitious research goals, while identifying intermediate milestones.
Directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.
Work with a large team.
Contribute to publications and open-sourcing efforts.
Mentor other team members. Play a significant role in healthy cross-functional collaboration.
Prioritize research that can be applied to Meta's product development.

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 in the board field of machine learning. 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 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 conferences and at leading journals.
Actively identify new opportunities within Meta's long term roadmap for applied science contributions.

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview
Come join the Intuit team as a Staff Data Science/MLEngineer.

Our Data Exchange group is responsible for acquiring millions of transactions and statements a day to satisfy our customers needs in all Intuit products.

You will utilize your skills to help develop and maintain machine learning models, using both analytical algorithms and deep learning approaches, that will be leveraged by critical backend systems of Intuits Data Acquisition Platform.

In this role, you will:

Lead and apply best practices in ML Project lifecycle management: scoping, data preparation, deployment, and monitoring
Collaborate with multiple teams at Intuit and contribute to components in different business units. We love engineers who lead the change, communicate with customers, and deliver the most beautiful and intuitive applications
Be expected to help architect, integrate, optimize, deploy, and code data pipelines and ML models at scale using the latest industry tools and techniques
#LI-Hybrid

Responsibilities
Design and build systems, which improve machine learning scalability, usability, and performance
Own end-to-end model development lifecycle, including defining problems, featurization in distributed process systems, model exploration, and development, deployment, and model performance monitoring
Collaborate with stakeholders to define success criteria and align model metrics with business goals
Work side-by-side with product managers, business analytics, data scientists, and backend engineers in enabling AI solutions for business use cases
Explore the state-of-the-art technologies and apply them to deliver customer benefits.
Roughly 50% hands-on coding