Role Description

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.

On-call work may be necessary occasionally to help address bugs, outages, or other operational issues, with the goal of maintaining a stable and high-quality experience for our customers.

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