Offer summary

Qualifications:

BS, MS, or PhD in Computer Science, Mathematics, Statistics, or related fields., 10+ years of engineering experience with 5+ years in building Machine Learning or AI systems., Proven experience with large-scale machine learning models, particularly Large Language Models (LLMs)., Strong software engineering skills in multiple languages, including Python, Go, and C/C++. .

Key responsabilities:

Design and implement ML-driven solutions to enhance search relevance and workflow automation.

Develop techniques to fine-tune and adapt LLMs for enterprise use.

Own the full ML lifecycle from data collection to model deployment and evaluation.

Collaborate with cross-functional teams to integrate AI capabilities into Dropbox's products.

Job description

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.)

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