Role Description
As a Machine Leaning Engineer on our Multimedia AI team, you will be involved in shaping the future direction of Dropbox Dash and pushing the boundaries on what the world thinks is possible by leveraging the latest advancements in AI/ML. You will join a team of top-tier Machine Learning Engineers and be an inherent part of the product org to create and build delightful new experiences.
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
- Work with large scale data systems, and infrastructure
- Help productionize multimodal and semantic retrieval systems at scale, powering Dash’s multimedia and creative search experiences.
- Partner with product, design, and infrastructure teams to improve retrieval, ranking, and conversational experiences across image, video, and text content.
- Build and iterate on quick prototypes and experimental features, driving innovation in multimodal interaction and creative workflows.
- Run quality and performance benchmarks across individual components and end-to-end systems to identify optimization opportunities.
- Contribute to open source projects and leverage OSS tools for efficient inference and scaling.
Requirements
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
- 5+ years of experience in engineering with 3+ years of experience building Machine Learning or AI systems
- 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, HuggingFace, TensorFlow, Keras, Scikit-learn, etc.)
- Familiarity with search-related applications of Large Language Models
- Proven experience in machine learning, multimodal AI, or search and ranking systems.
- Familiarity with semantic search, embeddings, vector retrieval, and optimizing ranking metrics such as nDCG or MRR.
- Experience running quality and performance benchmarks across components and end-to-end pipelines in large-scale machine learning and retrieval systems to identify and drive optimizations.
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, bayesian reasoning, recommender systems, learning to rank, speech processing, learning from semistructured data, graph learning, reinforcement or active learning, large language models, ML software systems, retrieval-augmented generation, machine learning on edge devices
- 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
