Senior Machine Learning Engineer, Dash Agentic AI

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Job Description

Role Description

As a Senior Machine Learning Engineer, you will play a key role in advancing Dropbox’s mission to create a more enlightened way of working. Leveraging cutting-edge AI/ML technologies, you will design, build, deploy, and refine highly reliable AI agents operating at massive scale. Your work will 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

Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.

Responsibilities

Design and productionize agentic AI frameworks — including multi-agent coordination, planning, tool-use, and memory — that allow agents to maintain long-term context and execute complex tasks across the Dropbox ecosystem.
Lead the end-to-end design of ML systems, from fine-tuning (SFT, RLAIF) and advanced prompting to inference optimization and production monitoring.
Establish rigorous safety, alignment, and evaluation frameworks to ensure our autonomous systems are helpful, honest, and harmless.
Collaborate across Product, Design, Infra, and Frontend teams to translate ambiguous user needs into concrete AI capabilities that move the needle for the business.
Mentor junior engineers and serve as a core contributor to the broader Dropbox AI strategy, fostering a culture of technical excellence.

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 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.

Preferred Qualifications

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.