דרושים » AI » Research Scientist - LTX Model Applications

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.

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.

The team focuses on art direction, prompt architecture, and technical experimentation, bridging the gap between creative design and AI tool development inside LTX Studio platform.

Its responsibilities include feature development, testing generative models, building LLM-driven systems for visual generation, and defining visual presets, using the team creative background to bring out-of-the-box thinking and artistic perspective to technical features and innovation.

What you will be doing
Own visual quality and creative evaluation across image or video domains within LTX Studio
Lead AI Evaluation & AI Creation efforts - testing new generative models, benchmarking outputs, identifying strengths and limitations
Build POCs using AI coding tools and vibe coding methodologies
Translate innovation ideas into demos, workflows, and product features (0→1 ownership)
Develop and refine prompt engineering systems, including prompt enhancement logic and structured generation rules
Improve and iterate on existing product features related to visual generation
Collaborate closely with PMs, developers, researchers, and cross-functional teams
Participate in feature ideation and bring creative-first thinking into technical product discussions
Help define visual presets, output standards, and quality benchmarks
Continuously explore emerging tools, models, and techniques in the AI creator ecosystem

Were looking for a Creative AI Solutions Specialist to join our AI Lab - a cross-functional group driving innovation, experimentation, and advanced AI capabilities across .

This role sits at the intersection of creative technology, product, and applied AI. Youll work on cutting-edge LTX-2 workflows, prototype new capabilities, help bring AI solutions to market, and act as a domain expert supporting teams across the company - from Product and Design to Sales and Business Development.

Success in this role requires strong ownership, curiosity, technical depth, and a passion for pushing creative AI forward.

What you will be doing
Build AI-driven creative workflows, PoCs, and production-ready pipelines (with a strong focus on LTX-2)
Experiment with generative AI to unlock new creative capabilities and solve high-end production challenges
Create workflows and custom LoRAs to showcase capabilities to clients and design partners
Partner with Business Development and major studios, providing technical demos and pipeline integration guidance
Act as a trusted technical advisor for clients and internal teams
Collaborate with Product, Research, Design, and Creative teams to turn innovation into impactful features
Own the ideation and exploration of new creative and AI-driven capabilities, driving initiatives from concept and research to tangible prototypes and production impact

our CTO group is looking for an outstanding physical-ai applied researcher to join our team.
the CTO group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. the group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy. we are seeking a researcher who thrives at the intersection of Machine Learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems. in this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. you will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. the problems are open-ended, scientifically challenging, and deployed at unprecedented scale. this is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. if youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of physical ai at our company. what will your job look like:
design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
lead research directions from conception to full-scale production.
develop using Python (pytorch or similar frameworks) as well as C ++/gpu/cuda.

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 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.

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.

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.

We're in search of an experienced and skilled Senior Data Engineer to join our growing data team. As part of our data team, you'll be at the forefront of crafting a groundbreaking solution that leverages cutting-edge technology to combat fraud. The ideal candidate will have a strong background in designing and implementing large-scale data solutions, with the potential to grow into a leadership role. This position requires a deep understanding of modern data architectures, cloud technologies, and the ability to drive technical initiatives that align with business objectives.

Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.

Your Arena

Design, develop, and maintain scalable, robust data pipelines and ETL processes
Architect and implement complex data models across various storage solutions
Collaborate with R&D teams, data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality solutions
Ensure data quality, consistency, security, and compliance across all data systems
Play a key role in defining and implementing data strategies that drive business value
Contribute to the continuous improvement of our data architecture and processes
Champion and implement data engineering best practices across the R&D organization, serving as a technical expert and go-to resource for data-related questions and challenges
Participate in and sometimes lead code reviews to maintain high coding standards
Troubleshoot and resolve complex data-related issues in production environments
Evaluate and recommend new technologies and methodologies to improve our data infrastructure