Research Scientist jobs in Tel Aviv-Yafo

Meet Harmony, Monday.com's new AI venture. We're redefining how businesses interact with their customers through next-generation conversational AI and agentic systems. Harmony doesn't just react- it plans, reasons, and acts. You'll join a small, highly autonomous team with the agility, ownership, and pace of a startup, working alongside world-class engineers and researchers to build the next generation of conversational AI. You'll have access to large-scale real-world data, the compute and resources to pursue groundbreaking research, and the freedom to move fast while helping shape our AI vision. About The Role - Research novel algorithms and design new deep learning architectures to advance our in-house NLP and conversational models. - Develop methods for translating customer goals into structured representations with controlled states and real-time observations. - Help design the reasoning layer that orchestrates our AI models efficiently alongside a range of LLMs, enabling each agent to adapt its policy throughout a conversation while remaining aligned with customer objectives. - Build analytical tools that trace our AI decision process across a vast corpus of conversations, providing clear visibility into their reasoning and behavior. - Collaborate closely with engineers and product teams to translate cutting-edge research into production at scale. - Proactively lead new ideas and technical initiatives. Requirements - MSc or PhD in Computer Science, Mathematics, Physics, Electrical Engineering, or a closely related field. - Proficiency in Python. - Strong theoretical background in machine learning and deep learning principles. - Solid understanding of NLP concepts and relevant linguistic principles (co-reference, natural language inference, grammatical relations, and similar). - Experience using statistics and information theory to evaluate AI models and conduct EDA. - 4+ years of experience conducting NLP research with deep learning models and frameworks (PyTorch, TensorFlow, and similar). - Experience shipping AI systems from research to production. - Experience with conversational AI agents is a big plus. - Publications at top-tier conferences (ACL, EMNLP, NeurIPS, ICML, CVPR) are a plus.

Description A winning ad could change the trajectory of a mobile game. The only way to consistently find winners is to explore more, faster, and smarter than anyone else. Sett is a UA creative performance platform for mobile gaming. We build agentic AI systems that analyze performance data, generate diverse playable and video ad concepts, deploy them to ad networks, and continuously learn from the results. The full creative loop. Automated, and done better than humans only. Our system doesn't remix templates. It creates fundamentally different creative hypotheses, each one built from real performance signals As a Lead Research Scientist at Sett, you’ll take scientific ownership of a challenging, open-ended research program, combining hands-on deep learning research with technical leadership and mentorship. Working closely with the Head of Research, you’ll turn a broad objective into a coherent research program: defining the problems, developing modeling approaches, designing experiments, and determining what constitutes meaningful progress. You’ll have substantial ownership of the scientific direction, alongside responsibility for advancing the work yourself and providing technical guidance and mentorship to others. This role requires someone who can get a difficult research project moving and keep it moving - not only by developing promising ideas, but by driving their own work and others’ forward through scientific, technical, and engineering obstacles. Responsibilities • Define and evolve the research program - Explore and formulate research questions that fit within company strategy, set scopes and priorities, and revise the framing as new evidence emerges. • Develop and investigate deep learning approaches - Design, implement, and train models, exploring architectures, representations, and learning objectives suited to the problem. • Build a rigorous experimental foundation - Establish baselines, shape data collection and dataset design, and develop evaluation methods that reveal model capabilities, limitations, and generalization. • Own the project’s progress - Identify and address obstacles, coordinate efforts with collaborators, and drive the next steps without waiting for a clear path. Judge when to deepen, change, or discontinue a direction. • Provide technical guidance and mentorship - Help other researchers develop their approaches, strengthen their scientific judgment, and work through difficult problems. Collaborate with Engineering and Product to translate validated research into usable capabilities. Requirements • A Ph.D. focused on deep learning, or equivalent research experience demonstrating comparable scientific and technical depth. • A track record of leading substantial deep learning research, with ownership of problem formulation, methodological choices, experimental direction, and outcomes. • Deep, hands-on model-building experience, including designing or substantially adapting architectures and training methods, implementing experiments in frameworks such as PyTorch or Jax, and diagnosing model behavior. • Strong research judgment: the ability to identify important questions, choose promising approaches, design informative experiments, and critically assess evidence. • Demonstrated ability to drive difficult projects forward, taking responsibility for both your own progress and the broader research effort. You can work resourcefully through obstacles, push and support collaborators, and sustain momentum on long-horizon problems with substantial uncertainty. • Technical depth, adaptability, and intellectual independence. You’ve gone deeply into challenging problems, can move across methods and domains, and form and revise your own views rather than relying on a prescribed approach. • Experience providing technical leadership and mentorship, helping others become more effective and independent researchers. Relevant backgrounds include world modeling, video or multimodal representation learning, sequential modeling, imitation learning, reinforcement learning, and modeling human behavior.