Large Scale Training Engineer - LTX Model

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 world’s 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.

Why Join Us

We’re here to push the boundaries of what’s possible with AI and video - not for the buzz, but for the craft, the challenge, and the chance to make something genuinely new.

We believe in an environment where people are encouraged to think, create, and explore. Real impact happens when people are empowered to experiment, evolve, and elevate together. At Lightricks, every breakthrough starts with great people and a collaborative mindset. If you're looking for a place that combines deep tech, creative energy, and zero buzzword culture, you might be in the right place.

About The Role

As a Large Scale Training Engineer, you will play a key role in enhancing the training throughput of our internal framework and enabling researchers to pioneer new model concepts. This role demands excellent engineering skills for designing, implementing, and optimizing cutting-edge AI models, alongside writing robust machine learning code and understanding supercomputer performance deeply. Your expertise in performance optimization, understanding distributed systems, and bug elimination will be crucial, as our framework supports extensive computations across numerous virtual machines.

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.
  
  

• Industry experience with small to large-scale ML experiments and multi-modal ML pipelines.
• Strong software engineering skills, proficient in Python, and experienced with modern C++.
• Deep understanding of GPU, CPU, TPU, or other AI accelerator architectures.
• Enjoy diving deep into system implementations to improve performance without compromising code quality and maintainability.
• Passion for driving ML large-scale training workloads efficiently and optimizing compute kernels.
• You are encouraged to apply if you meet 3 out of the 5 core qualifications above and are motivated to grow in the remaining areas.
  
  

• Background in JAX/Pallas, Triton, CUDA, OpenCL, or similar technologies.
• Familiarity with Kubernetes-based environments for running and scaling large-scale workloads.