Key skills
Machine learningSearch systemsRanking systemsRecommender systemsLarge language model architecturesPyTorchJAXPythonGoC++Large-scale distributed data systemsHigh-throughput production environmentsAnalytical skillsBias to actionCAIMachine LearningMLDeep LearningNLPLLMRAGAgenticCollaboration
About this role
Dropbox is seeking a Senior Machine Learning Engineer to advance their mission of creating a more enlightened way of working. This role involves designing and deploying AI agents that enhance collaboration and organization for millions of users.
Responsibilities:
- Leverage cutting-edge AI/ML technologies to design, build, deploy, and refine highly reliable AI agents operating at massive scale
- Power Dropbox Dash’s universal agentic search and autonomous organization features, transforming how millions of users collaborate, stay organized, and focus on the work that truly matters
- Participate in on-call rotations as part of the employment
Requirements:
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field (or equivalent work experience)
- 8+ years of software engineering experience, with at least 5+ years dedicated to building and deploying production-scale AI/ML systems
- Professional experience in ML modeling for complex systems such as Search, Ranking, or Recommender Systems
- Deep familiarity with LLM architectures and hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar)
- Strong proficiency in Python (required) and experience with systems languages like Go or C/C++. You should be comfortable building the infrastructure that surrounds the model
- Extensive experience working with large-scale distributed data systems and high-throughput production environments
- Exceptional analytical skills and a 'bias to action' when navigating ambiguous technical challenges
- PhD with a focus on Deep Learning, NLP, or Reinforcement Learning (RLHF/RLAIF)
- Proven track record of taking AI products from concept to launch, either at a massive scale (millions of users) or by leading multiple 0 → 1 cycles in a fast-paced environment
- Hands-on experience with autonomous agent frameworks, multi-step planning, tool-use (function calling), and advanced RAG
- Experience with inference optimization, model distillation, or fine-tuning techniques to improve performance and cost-efficiency