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!
Responsibilities
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
Requirements
BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or relatedwork 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.)
Preferred Qualifications
PhD in Computer Science or related field with research in machine learning
Experience building 0→1 ML products at large(dropbox-level) scale ormultiple 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)
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