Senior Machine Learning Engineer, Dash Agentic AI

Dropbox Dropbox · Enterprise · Canada +1 · Dash - Eng (Sub Team)

Senior Machine Learning Engineer to design, build, and deploy AI agents for Dropbox Dash's universal agentic search and autonomous organization features. Requires experience with production-scale AI/ML systems, LLM architectures, search/ranking/recommender systems, and building surrounding infrastructure. Preferred experience with agent frameworks, RAG, tool-use, inference optimization, and fine-tuning.

What you'd actually do

  1. Design, build, deploy, and refine highly reliable AI agents operating at massive scale.
  2. 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
  3. Building and deploying production-scale AI/ML systems.
  4. ML modeling for complex systems such as Search, Ranking, or Recommender Systems.
  5. Deep familiarity with LLM architectures and hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar).
  6. Building the infrastructure that surrounds the model.

Skills

Required

  • BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field (or equivalent work experience)
  • 8+ years of software engineering experience
  • 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
  • Hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar)
  • Strong proficiency in Python
  • Experience with systems languages like Go or C/C++
  • 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
  • Bias to action

Nice to have

  • 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)
  • Advanced RAG
  • Experience with inference optimization
  • Model distillation
  • Fine-tuning techniques to improve performance and cost-efficiency

What the JD emphasized

  • building and deploying production-scale AI/ML systems
  • ML modeling for complex systems such as Search, Ranking, or Recommender Systems
  • LLM architectures
  • building the infrastructure that surrounds the model
  • autonomous agent frameworks
  • multi-step planning
  • tool-use (function calling)
  • advanced RAG
  • inference optimization
  • model distillation
  • fine-tuning techniques

Other signals

  • design, build, deploy, and refine highly reliable AI agents operating at massive scale
  • power Dropbox Dash’s universal agentic search and autonomous organization features
  • building and deploying production-scale AI/ML systems
  • ML modeling for complex systems such as Search, Ranking, or Recommender Systems
  • LLM architectures
  • building the infrastructure that surrounds the model
  • autonomous agent frameworks, multi-step planning, tool-use (function calling), and advanced RAG
  • inference optimization, model distillation, or fine-tuning