Dropbox
Design and deploy machine learning systems for AI search and organization features at Dropbox, collaborating across teams to drive impactful outcomes.
Build state-of-the-art machine learning systems at Reddit to enhance advertiser experiences through automation and optimization. Collaborate with cross-functional teams to leverage live auction data and advanced techniques for boosting revenue and advertiser value.
reserv
Senior Machine Learning Engineer at Reserv, developing AI models and machine learning solutions for insurtech company
CAST AI
Design and deploy sophisticated data models at CAST AI, leveraging cloud-native technologies and contributing to machine learning operations. Collaborate with cross-functional teams and stay ahead of industry trends while enjoying benefits like flexible work hours and company equity.
Apollo.io
Build and productionize Machine Learning models for Apollo products like Search and Recommendations. Leverage data to predict user behaviors and optimize their experience. Improve ML stack and maintain scalable data pipelines.
Kueski
Design and build scalable pipelines for data preparation and automate machine learning models
Invisible Technologies
Senior Machine Learning Engineer at Invisible Technologies: Build robust backend systems, optimize cloud infrastructure, and collaborate on advanced ML technologies.
Entefy
Senior Machine Learning Engineer at Entefy: Unstructured data classification & clustering expertise required.
Splice
Senior Machine Learning Engineer for generative audio/music applications
Wasabi Technologies
Senior Machine Learning Engineer for AI/ML product Wasabi AiR, driving advancements in AI, computer vision, and NLP, with a focus on scalability and reliability.
Coursera
Join Coursera's Machine Learning team and contribute to creating personalized learning experiences through AI-powered technologies.
Quora
Develop innovative ML solutions for Quora's Poe platform, focusing on NLP, LLMs, and recommender systems. Collaborate with teams to build AI-driven features and contribute to the growth of a leading knowledge-sharing platform.
Spotify
Senior Machine Learning Engineer for real-time recommendations on Spotify's Now Playing View surface
Spotify
Optimize machine learning models for production use cases at Spotify, collaborate with a multidisciplinary team, and implement scalable Kubernetes clusters.
Coursera
Join Coursera's Discovery Science ML team to develop hyper-personalized recommender systems using machine learning techniques.
Welocalize
Develop machine learning solutions for localization and business workflow processes using Python and AWS services.
Welocalize
Develop and implement machine learning models for localization and business workflow processes using Python and AWS services.
Welocalize
Machine Learning Engineer for localization and business workflow processes using AWS services and Docker
hims & hers
Lead Machine Learning Engineer with 10+ years of experience in Engineering and/or Machine Learning
Dropbox
As a Senior Machine Learning Engineer at Dropbox, you will design and deploy large-scale ML systems to enhance features like AI search and organization. You'll collaborate with various teams and integrate cutting-edge advancements in AI/ML technologies.
As a Senior Machine Learning Engineer, you will play a key role in advancing Dropbox’s mission to create a more enlightened way of working. Leveraging cutting-edge AI/ML technologies, you will design, build, deploy, and refine large-scale machine learning systems. Your work will power Dropbox Dash’s universal AI search and AI-assisted organization features, transforming how millions of Dropbox users collaborate, stay organized, and focus on the work that truly matters.
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.
Design, build, evaluate, deploy and iterate on large scale Machine Learning systems
Understand the Machine Learning stack at Dropbox, and build systems that help Dropbox personalize their users’ experience. Develop and maintain production-quality code for serving machine learning models at scale
Work with Product, Design, Infrastructure and Frontend teams to bring your models, and features to life
Contribute to team’s technical strategy for the end-to-end machine learning lifecycle, ensuring alignment with business objectives and driving impactful outcomes
Explore and integrate the latest advancements in Search, LLMs, Recommender Systems, and Representation Learning into Dropbox's products
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.
BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
8+ years of experience in engineering with 5+ years of experience building Machine Learning or AI systems
Professional working experience in ML modeling for at least one of the following: Recommender Systems, Search, or Ranking
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
