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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.

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.

We are looking for a sharp and driven Data Solutions Analyst to join a newly formed team and help build the analytical foundations behind some of our strategic growth initiatives from the ground up. This is a unique opportunity to influence how new businesses operate and scale across Amazon, D2C, Paid Media, and emerging growth channels. You will have ownership over both the measurement frameworks and the solutions that drive business growth.

Working at the intersection of Data, Marketing, Product, and Business, you will identify opportunities, define success metrics, uncover insights, and build the solutions needed to turn those insights into action. This role combines analytical ownership with hands-on solution development, leveraging data, automation, AI, and modern tooling to solve business challenges end-to-end. Success in this role is measured not only by the quality of your analysis, but by your ability to translate business challenges into scalable solutions that drive measurable impact.

Roles and Responsibilities:
Build analytical infrastructure, measurement frameworks, and decision-support capabilities from the ground up for new and rapidly growing business units.
Evaluate performance across Amazon, D2C, Paid Search, Paid Social, and emerging channels, identifying growth opportunities, business risks, and areas for optimization.
Translate business challenges into scalable solutions by combining AI powered automation, agents, workflows, and building tools that drive business impact.
Design, build, and maintain dashboards, monitoring systems, analytical products, and self-service tools that enable teams to make faster and better decisions.
Own the Semantic Layer: Maintain and scale our unified BigQuery Semantic Layer, ensuring a single source of truth for business and analytics metrics.
Partner closely with Marketing, Product, and Business stakeholders to understand business needs and drive end to end solutions from idea to implementation.

we are looking for a Senior Software Engineer.
You will be responsible for building the "brain" of main product, which is a distributed file system -the layer that manages orchestration, metadata, cluster membership, and implements complex logic required to manage hundreds of thousands of nodes, for cluster scaling, automated failover, zero-downtime rolling upgrades and "self-healing" to detect and remediate hardware failures or network partitions.
Design, implement, unit test, and maintain services that coordinate and the cluster
Optimize Performance: Work with complex data structures like spanning trees or LSM-Trees, concurrency control mechanisms (MVCC) to improve speed and efficiency.
Collaborate closely with cross-functional teams to translate backend capabilities into real-world impact

we are looking for a Senior Software Engineer.
The group is a high-powered team responsible for implementing algorithms at scales of 100s of PBs. The team also manages the core filesystem components, including blocks and metadata management, snapshots, RAID logic, object-store tiering, unique cloud disaster recovery features, and more. And most importantly, they skillfully handle the most delicate part of the solution - our customers data.
As a Senior Software Engineer, youll:
Design and develop distributed file system components to support data management features such as snapshots, replication, tiering, and advanced data reduction algorithms;
Participate in the design, architecture, and implementation of next-generation storage architecture;
Assist in technically managing initial storage implementations including proofs-of-concept;
Diagnose bottlenecks and implement clean and performant solutions to achieve unbeatable performance;
Design algorithms and data structures to make sure customer data is safe and coherent across our solution in a wide variety of failure modes; and
Constantly revisit the architecture, algorithms, and methodologies to improve productivity, reliability, and maintainability.

We are looking for a Technical Lead to drive the architectural direction and engineering excellence of this group. This is a senior, deeply hands-on role for a technology leader who can own the technical roadmap, mentor a team of elite engineers, and build the infrastructure that challenges platform to its theoretical limits.
What You'll Lead:
Define and own the technical architecture of the group's distributed testing and reliability platform - designing for massive scale, real-world workload simulation, and adversarial failure injection
Lead effort involving multiple engineers, setting technical standards, running architecture reviews, driving design decisions, and mentoring engineers to grow
Build the systems that orchestrate millions of concurrent IO operations, inject chaos at the infrastructure layer (latency, packet loss, hardware failures), and expose the hardest-to-find race conditions and consistency bugs
Advance AI-driven approaches to test automation: intelligent scenario generation, LLM-augmented root-cause analysis, and autonomous validation pipelines
Drive observability and reliability engineering across the group - building telemetry pipelines that track P99 latency, jitter, and system health, turning quality into a quantitative discipline
Collaborate deeply with Core R&D, Storage Kernel, and Infrastructure teams - translating architectural knowledge into targeted reliability strategies
Establish engineering practices - design docs, production-grade code reviews, testing philosophy, and cross-team technical alignment