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Develop and productionize end-to-end machine learning models and ranking algorithms for search and personalization at scale. Collaborate with cross-functional teams to identify business opportunities and optimize marketplace performance.
You will set the technical direction for AI initiatives and design production-grade machine learning systems to power Confluence experiences. You will also lead the development of customer-facing AI capabilities while partnering with cross-functional teams to drive product strategy and engineering standards.
You will architect, build, and scale the infrastructure orchestration and distributed compute platform for decentralized model training. This includes managing multi-cloud deployments, ensuring fault-tolerant distributed ML systems, and handling real-world network conditions.
Innovate and develop machine learning algorithms to maximize advertising performance and ROI. Collaborate with data engineers to write production-ready code and prototype new algorithmic pipelines.
The role involves analyzing malware and detection data to improve machine learning model efficacy and manage false positives. You will investigate customer detection tickets and collaborate with internal teams to enhance threat detection capabilities.
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The AI/ML Architect SME will define and maintain a scalable, modular AI/ML architecture while implementing end-to-end pipelines for data ingestion, model training, and deployment. They will also integrate MLOps best practices and leverage Infrastructure-as-Code to manage cloud-based AI/ML infrastructure in compliance with federal standards.
The role involves architecting, deploying, and maintaining secure machine learning systems and enterprise CI/CD pipelines. You will also partner with cross-functional teams to build scalable AI solutions and computer vision systems.
The role involves owning end-to-end machine learning systems for forecasting, including model development, deployment, and monitoring. You will also build AI-assisted development workflows and partner with cross-functional teams to translate business needs into technical roadmaps.
You will lead the architectural design and development of a scalable machine learning platform to accelerate pricing R&D and production workflows. This involves collaborating with researchers to build robust infrastructure for feature stores, model orchestration, and automated deployment while mentoring senior engineers.
You will lead the development of a next-generation machine learning platform for pricing, focusing on infrastructure that accelerates the transition from research to production. This role involves hands-on technical leadership, mentoring engineers, and ensuring the reliability and scalability of production systems.
The AI/ML Architect SME will define and maintain a scalable, modular AI/ML architecture while implementing end-to-end pipelines for data ingestion, model training, and deployment. They will also integrate MLOps best practices and leverage Infrastructure-as-Code to manage cloud-based AI/ML infrastructure in compliance with federal standards.
The instructor will teach synchronous online courses in AI and machine learning while maintaining accurate student records and compliance with college policies. They are responsible for planning engaging instruction and staying current with industry trends to support student success.
Design and implement machine learning models for time-series and sequential data to extract structured signals from noisy sensor inputs. Build and optimize real-time inference pipelines while collaborating with cross-functional teams to transition models from research to production.
You will develop and deploy machine learning and AI solutions to improve automated outreach and agentic experiences. You will also collaborate with cross-functional teams to implement MLOps best practices and ensure model reliability.
You will provide technical leadership and mentorship to machine learning engineers while driving the technical strategy for content-based embeddings and relevance in ads. Additionally, you will develop and iterate on embedding models and LLM-based representations to improve ranking outcomes and ad delivery performance.
The Staff ML Engineer will lead technical strategy and cross-functional projects for ads foundational representations, including content embeddings and relevance modeling. They will also provide technical mentorship to team members while performing hands-on engineering, data analysis, and model development.
The engineer will build and deploy advanced machine learning models for ad targeting, identity resolution, and contextual signals. They will collaborate with cross-functional teams to productionize these models at scale within a high-performance advertising ecosystem.
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You will design and develop large-scale, fault-tolerant multimodal agentic experiences, including Voice AI and Deep Research capabilities. Additionally, you will lead complex, multi-team initiatives to bridge the gap between applied research and scalable engineering.
You will own and deliver end-to-end computer vision projects, including equipment defect detection and thermal anomaly identification for power grid infrastructure. Additionally, you will bridge the gap between research and production by adapting novel algorithms and building scalable data pipelines.
You will own and deliver end-to-end computer vision projects for power grid infrastructure, including defect detection and thermal anomaly identification. You will also bridge the gap between research and production by adapting novel algorithms and building scalable deployment pipelines.
You will manage and mentor a team of AI engineers while actively contributing to the codebase and designing GenAI systems. You will also partner with product managers to translate business problems into technical roadmaps and ship agentic AI solutions.
Design and improve machine learning models for ad ranking, relevance, and optimization while managing production inference systems. Collaborate with cross-functional teams to translate user experience gaps into measurable ML improvements using active learning and LLM-assisted workflows.
Develop and refine deep learning models for customer behavior prediction while collaborating with client data scientists. Optimize model training pipelines for efficiency and scalability while documenting technical approaches.
The Senior Machine Learning Engineer will independently develop and deploy scalable machine learning models while bridging the gap between research and production. They will also provide technical mentorship to data teams and ensure the effective implementation of end-to-end ML workflows.
Design, train, and deploy reinforcement learning systems to solve complex decision-making problems. Ensure the stability, safety, and scalability of RL solutions as they transition from research environments to production.
You will lead the design, development, and deployment of scalable machine learning models and predictive analytics pipelines. Additionally, you will collaborate with cross-functional teams to translate business challenges into data-driven solutions and mentor junior team members.
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You will build, evaluate, and improve machine learning ranking models to enhance search relevance and recall for private, team, and brand content. Additionally, you will collaborate with backend engineers to optimize indexing and investigate production regressions to ensure high-quality search outcomes.
Design, develop, and optimize Retrieval-Augmented Generation (RAG) models to solve complex problems for high-profile clients. Collaborate with cross-functional teams to deploy these models into production environments while ensuring scalable and maintainable code.
You will lead and manage a team of machine learning engineers to develop and deploy fraud detection models while maintaining strict performance SLAs. You will also drive cross-functional initiatives to improve data architecture and foster innovation across the product roadmap.
Design, develop, and deploy machine learning solutions to address complex economic challenges within the marketplace. Collaborate with cross-functional teams including product managers and data scientists to refine algorithms and improve operational efficiency.
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