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Role Description
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.
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
- 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. 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
- 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.)
- 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
Canada Pay Range
$219,300—$296,700 CAD
