Lead a team of Data Scientists, Analysts, and Data Engineers to develop scalable data science, AI, and analytics solutions that advance marketing science and innovation across the advertising industry.
Lead, mentor, and develop a team of 4+ Data Scientists, Analysts, and Data Engineers
Oversee the development and delivery of scalable data products and analytics solutions
Manage Databricks data architecture, including source-level tables, ETL/ELT pipelines, and production workflows
Lead the design and implementation of data integrations and delivery solutions, including APIs, SFTP, and Model Context Protocol (MCP) connections
Oversee the development and deployment of machine learning models for attribution, forecasting, fraud detection, anomaly detection, recommendation systems, and marketing optimization
Lead Engineer for Enterprise Applications responsible for driving design, development, modernization, and lifecycle management of enterprise application portfolios.
Responsibilities
Lead end-to-end delivery of web, mobile, and cloud-native applications
Define and govern technical architecture, development standards, and technology stack selection
Drive Agile execution including sprint planning, backlog refinement, and release management
Incorporate AI-assisted development tools to improve engineering productivity and quality
Evaluate and integrate AI/ML capabilities into applications
Requirements
Bachelor’s degree in computer science, Software Engineering, or related field (Master’s preferred)
Seeking senior/staff engineers to join the GenAI Search Platform team to improve search-based question answering systems using both traditional machine learning and cutting-edge generative AI technologies.
Compensation
For positions in this location, we offer a base pay of $176,100 - $308,200, plus equity (when applicable), variable/incentive compensation and benefits.
Responsibilities
Architect powerful, elegant abstractions to enable ranking and product teams to contribute safely and easily to the codebase.
Develop algorithm frameworks to support diverse conversational search applications such as multi-turn multi-hop question answering systems.
Drive the development of our search platform with new LLM-enabled generative AI features in a fast-paced environment.
Develop systematic evaluation metrics and methodology for our search models, leveraging LLMs to address unique challenges in the enterprise such as security and privacy.
Build a high-performing team with an inclusive and positive team culture.
Requirements
Nice to have: large language models (LLMs)
Nice to have: retrieval augmented generation (RAG)
Partner with Account Executives through the full sales cycle: discovery, solutioning, demos, proof of concepts, RFP responses, and handoff to the implementation team
Run structured discovery conversations that uncover the business value a prospect has not yet articulated for themselves, in their data, workflows, or current tech stack, and build that into a clear value case
Design and deliver product demonstrations that walk a prospect from that value case straight into the platform mechanics that deliver it, showing exactly how MoEngage's core capabilities and AI decisioning engine get them there
Lead technical validations and proof of concepts that prove out the value case with the prospect's own data, working with engineering and solutions architecture where needed
Write clear, accurate responses to RFPs and RFIs, including technical architecture and integration questions
Lead and grow a team of machine learning, backend, and data engineers, shaping technical strategy and execution for personalized listening experiences.
Compensation
The United States base range for this position is $227,495- $262,928 plus equity.
Responsibilities
Lead and grow a team of machine learning, backend, and data engineers
Shape the team's technical strategy and execution
Guide the development of machine learning and recommendation systems
Collaborate with Product and engineering partners to define priorities
Support the team in making thoughtful technical decisions across experimentation, scalability, system reliability, and model performance
Doing large scale machine learning and deep learning to improve Query Understanding and Ranking of Siri Search and developing fundamental building blocks needed for Artificial Intelligence.
Locations (2)
Beijing, Beijing, China
Shanghai, Shanghai, China
Responsibilities
Design and build infrastructures to support features that empowers billions of Siri, Spotlight, and Safari users.
Perform language processing, statistical analysis, and user intent analysis to support your hypothesis for how to improve product-outcomes.
Leverage proprietary parallel data processing platform to process web scale data to deliver product features and improvements.
Design and run/deploy various metrics and evaluations of features/improvements using a variety of tools like grading, logs processing, pre-launch and holdback A/Bs.
Present results of analysis to team and leadership across Apple.
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