לוח משרות דרושים בתל אביב יפו

קבוצה מגייסת מרכזי/ות קידום ייזום ובקרת תכנון.
התפקיד כולל:
ניהול מעקב וקידום תוכניות מפורטות ומתאריות, סקרים ועבודות תכנון אחרות, היזומות ע"י רמ"י
סיוע בקידום התכניות בהליכים הסטטוטוריים מול מוסדות התכנון, וכן טיפול בהסרת חסמי תכנון
קידום והפעלה של יוזמות תכנון חדשות, עריכה, תיאום והגשת בקשות לפרוייקטים חדשים
שירותים נוספים הנדרשים לבקרה, לייזום, למעקב ולקידום תכניות לפי דרישות הרשות.

לרשות מקרקעי ישראל דרוש/ה סטודנט/ית בעל/ת יתרת לימודים של שנתיים למרחב עסקי תל אביב
תפקיד: סטודנט/ית במרחב עסקי ת"א בתכנון ערים לסיוע במטלות צוות התכנון .
מקום עבודה: ת"א
שעות עבודה: עד 120 שעות בחודש (4 משמרות בוקר בשבוע)
שעות פעילות המשרד: ימים א'-ה' בין השעות 7:30-16:00
פרופיל התפקיד : סיוע במטלות מחלקת התכנון במרחב ת"א ברמ"י , שימוש במערכות מידע ממוחשבות גיאוגרפיות GIS ועוד.

data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. Were the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make. Through our products, partners and people, we make it easier for everyone to experience the world.

Leadership/Team Quote:
This opening is for the Content Intelligence team within the Central Tech department at Booking.com.
The Content Intelligence team is at the forefront of Generative AI innovation, driving solutions for travel-related chatbots, text generation and summarization applications, Q&A systems, and free-text search. Beyond this, the team is building a cutting-edge platform that processes millions of images and textual inputs daily, enriching them with ML capabilities. These enriched datasets power downstream applications, helping personalize the customer experiencefor example, selecting and displaying the most relevant images and reviews as customers plan and book their next vacation.

Role Description:
As a Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspectsensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. Were the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make. Through our products, partners and people, we make it easier for everyone to experience the world.

Leadership/Team Quote:
The Content Intelligence team builds the Content Intelligence Platform by consuming millions of images and textual inputs every day, and then enriching it with ML capabilities. Eventually, these will serve downstream applications and personalize our customers' experience (think of a way to choose and surface the right images and reviews when customers book their next vacation).
Moreover the team is taking a key role in the new GenAI project - The AI Trip Planner.

Role Description:
Youll work with top notch engineers and data scientists from the team on bringing it to the next level and enabling optimal user experience. The work will focus on building, training and deploying content models (Computer vision, NLP and Generative AI) using the most advanced technologies and models.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.