Responsibilities:

• Create and drive engineering agendas to advance multimodal capabilities, with emphasis on image and video generation, editing, understanding, controllable/long-horizon synthesis, agentic planning, RL training, and world simulation (including audio integration for richer video experiences).
• Improve data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, particularly for visual and audio data.
• Design evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence.
• Implement efficient algorithms for state-of-the-art model performance, including real-time inference, distillation, and scalable serving for visual content.
• Develop scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets.
• Collaborate cross-functionally to integrate AI solutions into production and rapidly iterate based on user feedback.

Basic Qualifications:

• Track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling.
• Experience in data-driven experiment designs, systematic analysis, and iterative model debugging.
• Experience developing or working with large-scale distributed machine learning systems.
• Ability to deliver optimal end-to-end user experiences.
• Hands-on contributor with initiative, excellence, strong work ethic, prioritization skills, and excellent communication.

Preferred Skills and Experience:

• Experience in SFT, RL, evals, human/synthetic data collection, or agentic systems.
• Proficiency in Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray, and related large-scale frameworks.
• Domain expertise in multimodal applications such as graphics engines, rendering techniques, image/video understanding and generation, world models, real-time simulation, or controllable/long-horizon visual content creation (audio/speech processing or music/audio generation experience is a plus where it supports video).
• Experience with agentic RL training, controllable/long-horizon generation, or multimodal agents that reason and act across modalities (especially in visual domains).

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What you'll do

The Senior Machine Learning Engineer will be responsible for architecting and building core components of the search platform and model stack. This will include designing, training, and optimizing large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models.

• Relentlessly push search quality forward — through models, data, tools, or any other leverage available
• Architect and build core components of the search platform and model stack
• Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models

What you need

The successful candidate will need to have a deep understanding of search and retrieval systems, including quality evaluation principles and metrics. They will also need to have a proven track record with large-scale search or recommender systems, and strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.

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