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1 ימים
Location: Haifa
Job Type: Full Time
As a Research Scientist in Model Evaluation, you are the ultimate authority on model quality and utility. You will design the automated judges, reward models, evaluation datasets, and benchmarking ecosystems that determine the future of LTX. Your mission is to provide the "ground truth" for our pre-training and post-training teams. You will blend the rigor of a researcher with the intuition of a product-thinker, developing metrics that capture both the aesthetic soul of a video and the functional precision required for high-stakes professional use.

Key Responsibilities
Steer Training & Research: Systematically evaluate model checkpoints to provide actionable insights that guide training experiments and architectural decisions.
Design Benchmark Ecosystems: Develop and run rigorous benchmarks for release candidates against competitive models, ensuring LTX-2 remains world-class.
Build Next-Gen Metrics: Develop robust automatic metrics and Reward Models (e.g., for RL, ITS, auto-research agents) that quantify complex attributes like temporal coherence, physical correctness, spatial accuracy, and foley synchronization.
Diagnose & Analyze: Perform deep root-cause analysis on model failures, providing the diagnostic clarity needed for researchers to implement targeted fixes.
Scale Evaluation: Collaborate with platform engineers to deploy evaluation frameworks across large-scale GPU clusters.
Requirements:
Technical Depth: Masters or PhD in Computer Vision, ML, or a related field, with strong software engineering skills and comfort in complex ML training environments.
The "Metric" Mindset: Deep expertise in evaluation methodology and statistical rigor. You know why standard metrics often fail and how to build better ones.
Perceptual Intuition: A sharp "eye and ear" for quality. You can articulate subtle nuances in motion or sound that automated systems might miss and use that intuition to improve our reward models.
Data-Driven Detective: You love diving into datasets to find the "why" behind the numbers, taking pride in curating and specializing data for specific evaluation tasks.
Product-Minded Scientist: You can think like an end-user. You care that our models don't just "beat the benchmark" but actually work reliably in professional pipelines.
Statistical Rigor: You understand experimental design, significance testing, and the nuances of perceptual quality assessment.
This position is open to all candidates.
 
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25/02/2026
חברה חסויה
Location: Tel Aviv-Yafo and Haifa
Job Type: Full Time
We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.
Requirements:
B.Sc. in Electrical Engineering - MUST
5+ years experience in algorithm development, preferably in complex, data-intensive environments
Deep expertise in Python, including statistical and machine learning packages
Proven experience across all algorithm development phases: research, implementation, testing, deployment, and debugging Familiarity with testing frameworks (e.g., unit tests, E2E tests, performance and memory profiling)
Intensive usage of AI coding tools like Cursor or equivalent (e.g., GitHub Copilot, CodeWhisperer)
Advantage
M.Sc. or higher in Electrical Engineering, Computer Science, or a related field
Prior experience with:
HW/SW integration or ATE/Chip testing
Version control systems (e.g., Git) and CI/CD tools
Distributed compute frameworks (e.g., Spark, MLRun)
Containerization and cloud orchestration (e.g., Kubernetes, cloud APIs)
Business intelligence tools (e.g., Tableau or equivalent)
AI platforms and agents that interface with model training, data annotation, performance monitoring, or autonomous debugging
Familiarity with chip production or design methodologies.
This position is open to all candidates.
 
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הגשת מועמדותהגש מועמדות
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עדכון קורות החיים לפני שליחה
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דיווח על תוכן לא הולם או מפלה
מה השם שלך?
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מודים לך שלקחת חלק בשיפור התוכן שלנו :)
25/02/2026
חברה חסויה
Location: Haifa
Job Type: Full Time
We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.
Requirements:
B.Sc. in Electrical Engineering - MUST
5+ years experience in algorithm development, preferably in complex, data-intensive environments
Deep expertise in Python, including statistical and machine learning packages
Proven experience across all algorithm development phases: research, implementation, testing, deployment, and debugging Familiarity with testing frameworks (e.g., unit tests, E2E tests, performance and memory profiling)
Intensive usage of AI coding tools like Cursor or equivalent (e.g., GitHub Copilot, CodeWhisperer)
Advantage
M.Sc. or higher in Electrical Engineering, Computer Science, or a related field
Prior experience with:
HW/SW integration or ATE/Chip testing
Version control systems (e.g., Git) and CI/CD tools
Distributed compute frameworks (e.g., Spark, MLRun)
Containerization and cloud orchestration (e.g., Kubernetes, cloud APIs)
Business intelligence tools (e.g., Tableau or equivalent)
AI platforms and agents that interface with model training, data annotation, performance monitoring, or autonomous debugging
Familiarity with chip production or design methodologies.
This position is open to all candidates.
 
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