Lead the design and implementation of end-to-end MLOps and LLMOps pipelines, from model development and training to deployment and continuous monitoring in production environments.
Responsibilities
Lead the design and implementation of end-to-end MLOps and LLMOps pipelines
Develop automation for model deployment and management
Implement and manage CI/CD pipelines for both ML and LLM models
Collaborate with data scientists, ML engineers, and software developers
Build data pipelines and workflows for training and serving models
Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
Nice to have: Experience with AI and Generative AI technologies
Nice to have: Experience with large language models (GPT, BERT)
Definir e evoluir arquiteturas de backend, considerando escalabilidade, resiliência, performance e manutenibilidade.
Desenvolver soluções em Python aplicando boas práticas de engenharia e padrões arquiteturais, como arquitetura hexagonal.
Projetar e evoluir agentes de IA utilizando LLMs, frameworks especializados e serviços como Amazon Bedrock.
Contribuir para a evolução de serviços em Go, considerando conceitos de concorrência e desempenho.
Atuar na arquitetura e infraestrutura cloud, utilizando serviços AWS, Kubernetes e Terraform, buscando eficiência operacional, escalabilidade e otimização de custos.
Build and shape Claude Design, a visual collaboration tool powered by AI, creating real‑time editing, design‑system inference, and AI‑driven generation experiences.
Locations (2)
San Francisco, California, United States
New York City, New York, United States
Compensation
Annual Salary: $405,000—$485,000 USD
Responsibilities
Build and ship user‑facing features across our products
Set technical direction for editor and canvas architecture, real‑time collaboration, and import/export
Own the quality bar for product look and feel
Partner with researchers to prototype vision and generation capabilities
Senior Data Engineer responsible for designing, building, and optimizing data infrastructure for analytics, technology platforms, and marketing solutions.
Locations (3)
Brazil
Colombia
Mexico
Responsibilities
Design, develop, and maintain scalable data architectures, warehouses, and ETL/ELT pipelines
Build and optimize data pipelines using Python, Apache Airflow, Snowflake, and AWS
Develop and maintain data models, schemas, and taxonomies
Evaluate architectural approaches and recommend solutions
Integrate structured and unstructured data from APIs, databases, advertising platforms, and other sources
Lead front‑end architecture and development for Coupang’s global web platform, driving performance, scalability, and cross‑market consistency.
Responsibilities
Lead design and development of Coupang’s global web platform, delivering scalable, high‑performance, maintainable PC and mobile solutions.
Serve as the primary reviewer and decision‑maker for web design, architecture, and technical project proposals, ensuring alignment with engineering standards and strategic goals.
Act as the technical lead for projects, driving successful delivery in a global environment.
Lead complex, multi-disciplinary engineering projects for data center construction and delivery, managing schedules, budgets, and vendor relationships.
Responsibilities
Manage a large, campus-wide data center construction and delivery organization
Support development of detailed, localized designs
Drive procurement, construction, commissioning, close out, and hand-off to Operations
Lead problem-solving engagement with partners
Establish and maintain delivery models and global relationships with vendors
Requirements
Bachelor's degree in a technical field
Nice to have: multi-group project management
Nice to have: people management
Nice to have: international construction projects
Nice to have: mission critical facilities experience
Lead a team of software engineers focused on AI/ML infrastructure and performance engineering, driving technical leadership, project delivery, and cross‑team collaboration across Google’s global ML stack.
Responsibilities
Learn and build an intuitive understanding of existing data collection, analysis, and visualization workflows with deep introspection across Frameworks, Accelerated Linear Algebra (XLA) and runtime stack.
Support new and exciting ML paradigms (such as horizontal scaling for upcoming TPU chips) by making contributions across the end to end stack and analysis tools.
Partner with ML Stack leads to understand model optimization use cases and bring debugging to feel native in 3P environments (VSCode, Cursor, Grafana, etc.).
Work with OSS ML inference frameworks such as vLLM, SGLang to provide insights in Xprof about performance improvement opportunities.
Partner with other teams that own various parts of the ML stack to understand performance optimization use cases.
Requirements
Bachelor's degree or equivalent practical experience
Define and document the microarchitecture for digital designs within the TPU
Partner with Verification to develop test plans and debug RTL, and collaborate with Physical Design to achieve timing, area, power, and manufacturability goals
Drive critical power optimization and automation initiatives across all on-chip-network components and subsystems
Support post-silicon validation and hardware debugging efforts to ensure successful deployment
Lead the development and enhancement of internal design tools, flows, and engineering methodologies
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