Senior Machine Learning Engineer, Dropbox

• 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.)

Desirable

• 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
• 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