Role Description

As a Staff Machine Leaning Engineer for this new division, you will be able to drive the future direction of a new initiative and push the boundaries on what the world thinks is possible by leveraging the latest advancements in ML. You will join a team of top-tier Machine Learning Engineers and be an inherent part of the product org to create and build delightful new experiences.

As a ground floor opportunity for this startup team, we need this staff engineer to build a top-tier truth-seeking culture with a lot of focus on impact and execution!

• You will serve as the ML thought leader to adopt and get ahead in the fast evolving landscape of ML
• You will design, build, evaluate, deploy and iterate on machine learning models with thousands to billions of parameters (or more!)
• You will push the limits of whats considered possible at scale and generate gains by leveraging the latest development in AI
• You will work across teams and functions to bring your models, and features not considered possible before to life

• BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
• 10+ years of engineering experience with 6+ years building Machine Learning or AI systems
• Experience working in a start-up or in a start-up like environment
• Strong industry experience working with large scale data systems
• Strong analytical and problem-solving skills
• Familiarity with the latest development in LLMs
• Proven software engineering skills across multiple languages including but not limited to Python
• Experience with Machine Learning software tools and libraries (e.g. TF, PyTorch, HuggingFace, LangChain etc.)

• PhD in Computer Science or related field with research in machine learning
• Experience building 0→1 ML products at large (dropbox-level) scale or multiple 0→1 products at smaller scale including experience with large-scale product systems
• Experience with one or more of the following: Recommendation Systems, Search, Large Language Models (LLMs)