Senior Machine Learning Engineer, Dash

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. You will be involved in shaping the future direction of the organization and pushing the boundaries on what the world thinks is possible. Leveraging cutting-edge AI/ML technologies, you will design, build, deploy, and refine large-scale machine learning systems. Your work will power Dropbox Dash’s universal AI search and AI-assisted organization features, transforming how millions of Dropbox users collaborate, stay organized, and focus on the work that truly matters.  

Collaborating closely with cross-functional teams, you'll leverage your ML expertise to tackle audacious challenges. Your contributions will directly impact millions of users, as every line of code you write furthers our mission to revolutionize the way people work and collaborate.

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, build, evaluate, deploy and iterate on large scale Machine Learning systems and LLM systems across cloud and mobile/edge environments.
• 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.
• Lead end-to-end LLM workflows: data curation, prompt engineering, retrieval-augmented generation (RAG)pipelines, tool use/agents, and fine-tuning (e.g., instruction tuning, LoRA/adapters) with rigorous evaluation.
• Optimize for latency, cost, and quality using techniques like quantization, distillation, caching, batching, and autoscaling; tailor models for on-device vs. cluster execution.
• Establish robust offline/online evaluation: experiment design, A/B testing, guardrails and safety checks, hallucination mitigation, and automated monitoring/observability with clear SLOs.
• Communicate technical trade-offs, risks, and impact to cross-functional stakeholders; write clear design docs, roadmaps, and decision records.
• Mentor teammates, contribute to code reviews and best practices, and help shape the technical direction of ML and AI at Dropbox.

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
• 9+ 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