Staff Machine Learning Engineer, Dropbox

• If you're excited about turning complex technical challenges into intuitive solutions at scale, join our Engineering team
• BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
• 10+ years of experience in engineering with 5+ years of experience building Machine Learning or AI systems
• Designed, fine-tuned, or deployed large-scale machine learning models, including Large Language Models (LLMs), for production use in a real-world application
• 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 Staff Machine Learning Engineer at Dropbox, you will lead the development of AI-powered intelligent systems that leverage and innovate on large language models (LLMs) to power search relevance and ranking, conversational AI, content creation, automation, and workflow intelligence
• You will architect and build complex, scalable ML systems that integrate enterprise-wide context, fine-tune models, and develop new capabilities beyond existing LLM paradigms
• Your work will directly impact Dropbox’s ability to deliver cutting-edge AI-first experiences to users and businesses
• You will collaborate across engineering, product management, design, and user research to push the boundaries of what’s possible with AI, ensuring that Dropbox remains at the forefront of innovation
• Build and Scale AI-powered Systems: Design and implement ML-driven solutions that enhance search relevance, ranking, document understanding, conversational AI, and workflow automation
• LLM Fine-tuning & Customization: Develop techniques to fine-tune, adapt, and enhance LLMs to make them enterprise-ready, ensuring optimal performance for Dropbox’s use cases
• Innovate on top of LLMs: Push the boundaries of multi-modal AI, retrieval-augmented generation (RAG), in-context learning, and agentic AI to create new product capabilities
• End-to-End AI Development: Own the full ML lifecycle, from data collection and preprocessing to model training, deployment, and continuous evaluation of AI models in production
• Technical Strategy & Leadership: Define the multi-year AI/ML roadmap, making key architectural and modeling decisions that align with Dropbox’s long-term vision
• Cross-functional Collaboration: Partner with engineers, designers, product managers, and researchers to integrate AI capabilities into Dropbox’s core product offerings
• Stay at the Cutting Edge: Keep up with state-of-the-art AI research, evaluating and incorporating advances in deep learning, transformers, multi-modal learning, and foundation models into Dropbox’s AI stack
• Drive AI-powered Product Innovation: Identify and propose novel product features that can be built with LLMs, working closely with product teams to bring AI-powered experiences to Dropbox users
• 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