• LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
• Inference / serving (e.g. vLLM)
• Strong foundation in machine learning and modern neural network architectures.
• Hands-on experience with training, fine-tuning, or deploying ML models
• Ability to write clean, production-quality code
• Comfort working across abstraction layers (model → infra → product)
• Strong problem-solving skills in ambiguous, fast-moving environments
• Bias toward shipping, iteration, and continuous improvement
• ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes addressed.
• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
• Iterations on models and systems are driven by real-world signals and measurable improvements.
• The best products today in the world were built by small, world class teams. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical AI product.