Research Scientist – LTX Model Evaluation

Who we are

Lightricks is an AI-first company creating next-generation content creation technology for businesses enterprises and studios with a mission to bridge the gap between imagination and creation. At our core is LTX-2 an open-source generative video model built to deliver expressive high-fidelity video at unmatched speed. It powers both our own products and a growing ecosystem of partners through API integration.

The company is also known globally for pioneering consumer creativity through products like Facetune one of the worlds most recognized creative brands which helped introduce AI-powered visual expression to hundreds of millions of users worldwide. We combine deep research user-first design and end-to-end execution from concept to final render to bring the future of expression to all.

The Team

Lightricks is an AI-first company creating next-generation content-creation technology for businesses enterprises and studios with a mission to bridge the gap between imagination and creation. At our core is LTX-2 an open-source generative video model built to deliver expressive high-fidelity video at unmatched speed. It powers both our own products and a growing ecosystem of partners through API integration.

Following the success of LTX-2 our widely adopted open-source text-to-audiovideo model we are expanding our efforts to develop cutting-edge audiovideo generation models and are hiring Research Scientists to join our Model Evaluation team - part of LTX Foundational Model group.

The Model Evaluation team is the central nervous system of the LTX Foundation Model group. We dont just measure performance; we define what good looks like across a vast array of use cases. While we power the next generation of creative tools LTX is also a foundational engine for simulation pipelines game engines synthetic data generation architectural rendering and digital avatars. We act as the critical bridge between raw research and industrial-grade reliability building the benchmarks that ensure our models are world-class for both artists and engineers.

The Role

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.

Ideal Candidate

• 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 dont 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.