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2 ימים
Location: Jerusalem
Job Type: Full Time
We are seeking a researcher to join the LTX Model Applications team, focusing on advancing and extending the capabilities of our LTX-Video models. You will design and explore new use cases, train novel ways to control the generation process, and push the boundaries of generative video technology. Your work will directly influence millions of users through our apps and contribute to the broader research and open-source communities, shaping the next generation of creative tools.
What you will be doing:
Advance and extend the capabilities of our LTX-Video models through new controls, features, and use cases.
Design and implement machine learning models for video, image, and audio generation, as well as multimodal applications.
Conduct experiments, prototype concepts, and validate ideas to expand generative video technology.
Collaborate with engineers, designers, and product managers to translate research into impactful product features.
Regularly open-source code, models, and research insights, contributing to the academic and open-source communities.
Stay at the forefront of generative AI research in video, image, and audio, and bring relevant advances into our products.
Write high-quality, efficient code and maintain best practices in research workflows.
Mentor other researchers and lead collaborative projects.
Requirements:
M.Sc. in computer science or a related field; Ph.D. preferred.
Strong background in generative AI, with familiarity in video and image generation and processing.
Demonstrated ability to originate ideas and drive projects from concept to implementation.
Familiarity with the latest research in generative AI and motivation to stay at the cutting edge.
Independent, proactive, and collaborative, with strong communication skills.
Preferred qualifications
Publications in relevant fields.
Experience writing production-grade algorithms with a focus on efficiency and runtime.
Familiarity with audio generation.
This position is open to all candidates.
 
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2 ימים
Location: Jerusalem
Job Type: Full Time
we are looking for a Research Scientist .
As a Large Scale Video Understanding Research Scientist, you will play a key role in improving video generation quality and efficiency by improving video and audio understanding pipelines used for both training data construction and model evaluation.. This role demands hands-on work with large-scale Video Language Models (VLLMs), including fine-tuning, post-training, and control, alongside implementing classic computer vision and signal processing algorithms and applying strong research skills. Your expertise in post-training and controlling large scale foundational models, understanding statistics, implementing complex systems and eliminating bugs will be crucial, as our video training sets consist of petabytes of data processed across hundreds to thousands of virtual machines.
What you will be doing:
Fine-tune and control VLLMs for video and audio understanding.
Design algorithms for balancing, filtering, and curating training and evaluation datasets, informed by model behavior and failure modes.
Implement classic and modern algorithms for processing, clustering, evaluation and filtering of large scale datasets.
Work within high-performance, scalable distributed systems capable of handling petabytes of data, with attention to throughput, correctness, and reproducibility..
Collaborate with other researchers and product stakeholders to iteratively improve training sets and evaluation protocols through tight feedback loops driven by model performance.
Requirements:
Experience training, fine-tuning, or post-training large-scale VLLMs or multimodal foundation models.
Strong software engineering skills, proficient in Jax or PyTorch.
Ability to develop and implement computer vision models for data filtering and evaluation.
Understanding of relevant topics in statistics, clustering.
Enjoys delving into system implementations to enhance performance and maintainability.
This position is open to all candidates.
 
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Location: Jerusalem
Job Type: Full Time
Required Algorithm Researcher - Autonomous Driving (CTO Group)
The CTO Group is a small, elite team focused on the decision-making module of our autonomous driving system.
Our mission is to solve some of the hardest problems in autonomous driving by combining the full spectrum of modern computer science - from rigorous mathematical modeling, optimization, and algorithm design to deep learning.
The team develops the driving policies that translate system-wide inputs into safe, efficient, and human-like driving behavior. Our work spans classical algorithms, optimization under constraints, probabilistic reasoning, reinforcement & supervised learning, all grounded in real-world driving data.
The group is comprised of top researchers and developers and is led by world-class scientists, offering a unique opportunity to work closely with the CTO, shape the future of autonomous driving, and influence the company's technical direction.
What will your job look like:
Research and develop innovative algorithms for autonomous driving planning and decision-making.
Model complex real-world driving scenarios and formulate them as optimization, learning, or algorithmic problems.
Apply a broad range of techniques - from mathematical modeling and classical algorithms to machine learning and deep learning - to improve driving policies.
Analyze large-scale driving data to gain insights, validate hypotheses, and improve system robustness and performance.
Design and implement high-quality production-ready code in a research-driven environment.
Read, evaluate, and build upon state-of-the-art scientific literature in algorithms, AI, and machine learning.
Requirements:
5+ years of experience in algorithm development, machine learning, or AI research.
Outstanding MSc in Computer Science, Computer Engineering, Mathematics, Physics, or a related quantitative field.
PhD - an advantage.
Strong programming skills in C++ and Python.
Outstanding mathematical and analytical abilities.
Passion for solving challenging research problems and the ability to quickly learn from scientific literature.
This position is open to all candidates.
 
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