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5 ימים
Meta
Location: Tel Aviv-Yafo
Job Type: Full Time
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.
Requirements:
Minimum Qualifications
A PhD in AI, computer science, or related technical fields.
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Experience holding an industry, faculty, or government researcher position.
Publications in machine learning, computer vision, NLP, speech
Experience writing software and executing complex experiments involving large AI models and datasets.
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
Preferred Qualifications
Direct experience in generative AI and LLM research.
First (joint) author publications experience at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL).
Fluent in Python and PyTorch (or equivalent)
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100934
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דיווח על תוכן לא הולם או מפלה
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מודים לך שלקחת חלק בשיפור התוכן שלנו :)
5 ימים
Meta
Location: Tel Aviv-Yafo
Job Type: Full Time
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.
Requirements:
Minimum Qualifications
Currently holds a PhD in the field of Computer Science, Data Science, or a related field from a top-tie academic institution.
At least 5 years of industry experience as an applied research scientist or a similar role, studying and developing machine learning technologies.
Experience with empirical research and for answering questions with data.
Experience developing algorithms in languages like Python, C, C++ or Java.
Experience analyzing datasets using languages like Python.
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
Preferred Qualifications
Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
Experience working and communicating cross functionally in a team environment.
Experience analyzing large datasets using tools like Presto, Hive or Spark.
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading journals or conferences such as ICML, NeurIPS, ICLR, IJCAI, AAAI, KDD, WWW, JMLR, JACM or similar.
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100929
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דיווח על תוכן לא הולם או מפלה
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מודים לך שלקחת חלק בשיפור התוכן שלנו :)
6 ימים
Intuit
Location: Petah Tikva
Job Type: Full Time
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
Requirements:
6+ years industry experience bringing AI models from modeling to production
Experience in modern, advanced analytical tools and programming
Expertise and experience in data mining algorithms and statistical modeling techniques such as classification, regression, clustering, anomaly detection, and text mining
Strong understanding of the Software design and architecture process
Experienced with working in cloud production-grade high-scale microservices environment
Languages such as Python, Java, or Scala
Expertise with Spark for data manipulation and featurization
Efficient in SQL, Hive, or SparkSQL
Comfortable in Linux environment
Familiar with backend services: K8S, Spring Boot, Vert.x, Docker
Excellent oral and written English communication skills: demonstrated ability to explain complex technical issues to both technical and non-technical audiences
BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research), or equivalent work experience
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