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We are looking for a Creative Designer to join the LTX Studio team.

In this role, youll create high-quality visual and motion assets that help bring our product to life in the experience and beyond. Youll work on in-product creative surfaces such as banners, pop-ups, and other visual components, while also supporting product demos, partner presentations, and broader product storytelling.

What you will be doing
Design high-quality visual and motion assets for LTX Studio
Create in-product assets such as banners, pop-ups, and visual components
Produce motion design assets using Adobe tools, especially After Effects
Support product demos and partner presentations with compelling visuals
Collaborate cross-functionally with Product Design, Brand, and Product teams
Maintain a strong visual identity and high design quality across all assets
Iterate quickly as the product evolves and new needs emerge

As an ML Software Engineer with a focus on low-level and CUDA-based optimizations, you will play a key role in shaping the design, performance, and scalability of machine learning inference systems. Youll work on deeply technical challenges at the intersection of GPU acceleration, systems architecture, and ML deployment.
Your expertise in CUDA, C/C++, and performance tuning will be crucial in enhancing runtime efficiency across heterogeneous computing environments. Youll collaborate with designers, researchers, and backend engineers to build production-grade ML pipelines that are optimized for latency, throughput, and memory use, contributing directly to the infrastructure powering next-generation AI products.This role is ideal for an engineer with strong systems-level thinking, deep familiarity with GPU internals, and a passion for pushing the boundaries of performance and efficiency in machine learning infrastructure.

Responsibilities
Design and implement highly optimized GPU-accelerated ML inference systems using CUDA and low-level parallelism techniques
Optimize memory, compute, and data flow to meet real-time or high-throughput constraints
Improve the performance, reliability, and observability of our inference backend across diverse compute targets (CPU/GPU)
Collaborate with cross-functional teams (including researchers, developers, and designers) to deliver efficient and scalable inference solutions
Contribute to ComfyUI and internal infrastructure to improve usability and performance of model execution flows
Investigate performance bottlenecks at all levels of the stack-from Python to kernel-level execution
Navigate and enhance a large, complex, production-grade codebase
Drive innovation in low-level system design to support future ML workloads

As the ML Platform Tech Lead, youll be responsible for the development of AI infrastructure, taking an active role in mentoring backend engineers directly.

This is a hands-on tech- lead position that blends system design and technical strategy. You will be expected to work closely with our ML research teams, product, and engineering stakeholders to take cutting-edge AI models into production, powering features across our creative apps used by millions worldwide.

You will own the full development lifecycle, from system architecture through deployment and operation, while also being accountable for team delivery, collaboration, and growth.

You will lead the web design efforts for LTX, our complete B2B ecosystem. In this role, you will shape how our professional video platform and AI models are presented to the world. You will serve as the strategic design voice connecting creative vision with business goals, ensuring our enterprise presence reflects the power and sophistication of the technology behind it.

Your responsibilities will include:

Managing and mentoring a team of web designers working on the LTX B2B ecosystem, supporting their professional growth and overseeing day-to-day creative output.
Owning the art direction for our enterprise web presence, ensuring a refined, consistent, and high-end look across platforms and models.
Acting as the primary bridge between Design and cross-functional stakeholders, driving alignment and clear communication around goals and priorities.
Driving data-informed design decisions by leveraging analytics, A/B testing, and performance insights to optimize user journeys and support SEO goals.
Implementing AI-driven workflows and helping the team adopt generative tools to enhance creativity, efficiency, and scale.
Collaborating closely with developers on our Webflow infrastructure to ensure designs are feasible and executed to a high standard.

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.

The Model Evaluation team is the central nervous system of the LTX Foundation Model group. We don't 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.

This role is designed for individuals who are not only technically proficient but also deeply passionate about pushing the boundaries of AI and machine learning through innovative engineering and collaborative research.

Key Responsibilities
Profile and optimize the training process to ensure efficiency and effectiveness, including optimizing multimodal data pipelines and data storage methods.
Develop high-performance TPU/GPU/CPU kernels and integrate advanced techniques into our training framework to maximize hardware efficiency.
Utilize knowledge of hardware features to make aggressive optimizations and advise on hardware/software co-designs.
Collaboratively develop model architectures with researchers that facilitate efficient training and inference.
Design, maintain, and evolve a high-quality, shared codebase that emphasizes correctness, readability, extensibility, testing, and long-term maintainability, while balancing performance requirements.

This role focuses on pioneering model architecture and pre-training algorithms, shaping the next generation of our foundational generative AI models.

What you will be doing
Pre-train and fine-tune video, audio, and image generative models to pursue state-of-the-art results.
Publish papers and open source models to benefit the research community and advance the field.
Design and implement machine learning models for text-to-audio and text-to-video generation.
Collaborate with data engineers to curate and preprocess text and video data.
Optimize models for high performance, ensuring efficient training and inference.
Build new controls and capabilities into generative text-to-audio and text-to-video models.
Stay updated with the latest developments in Generative AI, particularly in the fields of image, video, and audio.
Work closely with product teams to integrate AI models into applications and services.
Conduct experiments and prototype new concepts to advance the capabilities of our AI tools.

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.