Lead Research Scientist

sett · telaviv

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 builds agentic AI systems that take over the full creative loop to find winners. Automated, and done better than humans only.      We’re building a client-facing platform where game studios work directly with our AI Agents. This is a customer-facing, zero-to-one product role. You'll spend real time with clients, own adoption as your core metric, and hold a high bar for craft - this product competes with Slack for busy people's attention, and it only wins by being genuinely better. 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.

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