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You&#39;ll work through some of the most challenging multimodal problems that exist today while shipping improvements to customers daily.</p>\n<p>Responsibilities:</p>\n<p>Model Development &amp; Training: Train, fine-tune, and evaluate image generation models (diffusion, GAN, transformer-based). Implement and adapt techniques from research papers into working production systems. Design and run experiments to improve image quality, diversity, and controllability. Curate, clean, and manage large-scale image-text training datasets.</p>\n<p>Evaluation, Hillclimbing &amp; Quality Systems: Build and maintain evaluation frameworks for correctness, safety, grounding, and UX quality. Run hillclimbing loops across prompts, models, and tool-use strategies to continuously improve assistant performance. Analyze failure modes, design mitigations, and drive systematic improvements across the stack.</p>\n<p>LLM Tooling &amp; Internal Infrastructure: Develop internal tools for prompt experimentation, model comparison telemetry and debugging automated eval pipelines. Create reusable frameworks that accelerate the entire AI org’s ability to ship high-quality assistant features.</p>\n<p>Applied ML &amp; Product Integration: Integrate LLMs with product surfaces, APIs, and backend systems. Build lightweight ML components (ranking, classification, summarization, personalization) that enhance assistant intelligence. Collaborate with PM, design, and research to turn ambiguous ideas into polished user experiences.</p>\n<p>High-Velocity Teamwork: Operate with startup-founder energy: bias for action, rapid iteration, and comfort with ambiguity. Work closely with researchers, engineers, and product leaders in a fast-moving AI team where ideas ship quickly and impact is immediate. 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