Senior Machine Learning Engineer, Dash - Ranking and Recommendations

Role Description

Responsibilities

• Design, build, evaluate, deploy and iterate on large scale Machine Learning systems
• Understand the Machine Learning stack at Dropbox, and build systems that help Dropbox personalize their users’ experience. Develop and maintain production-quality code for serving machine learning models at scale
• Work with Product, Design, Infrastructure and Frontend teams to bring your models, and features to life
• Contribute to team’s technical strategy for the end-to-end machine learning lifecycle, ensuring alignment with business objectives and driving impactful outcomes
• Explore and integrate the latest advancements in Search, LLMs, Recommender Systems, and Representation Learning into Dropbox's products

Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.

Requirements

• BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
• 8+ years of experience in engineering with 5+ years of experience building Machine Learning or AI systems
• Professional working experience in ML modeling for at least one of the following: Recommender Systems, Search, or Ranking
• Strong industry experience working with large scale data
• Strong collaboration, analytical and problem-solving skills
• Familiarity with the state-of-the-art in Large Language Models 
• Proven software engineering skills across multiple languages including but not limited to Python, Go, C/C++
• Experience with Machine Learning software tools and libraries (e.g., PyTorch, Scikit-learn, numpy, pandas, etc.)

Preferred Qualifications

• PhD in Computer Science or related field with research in machine learning
• Experience with one or more of the following: Natural Language Processing, Deep Learning, Recommender Systems, Learning to Rank, Speech Processing, Learning from Semi-structured Data, Graph Learning, Large Language Models, and Retrieval-Augmented Generation
• 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 

Compensation