• Atuar com desenho e implementação de soluções GenAI/Agentic utilizando Google Cloud ADK (planejamento de agentes, tool use, orquestração); • Construir workflows de agentes para extrair metadados de fontes estruturadas e não estruturadas e gerar descritores compatíveis com ODPS (YAML/JSON); • Integrar entregáveis com Dataplex (registro em catálogo, empacotamento/exportação de metadados, versionamento quando aplicável); • Implementar gates de validação: validação de schema, verificação de campos obrigatórios e suporte a processos human-in-the-loop (Publish Gate); • Colaborar com times internos para garantir aderência aos padrões de dados, qualidade e governança; • Desenvolver mecanismos de avaliação e mensuração de outputs de IA (métricas de qualidade, test sets, ciclos de iteração).

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company’s three core products are Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles . Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. We’re looking for a Staff Machine Learning Engineer to join the Ad Ranking team at Snap! What you’ll do: Set and drive the technical direction for the Ad Ranking team and advance the core ML capabilities of the ad optimization stack Design, build, and deploy machine learning solutions to support Snap's monetization strategies Own outcomes for large, cross-team ML initiatives, from problem definition through long-term iteration and impact Establish technical vision and roadmaps, and resolve complex technical tradeoffs across teams Influence and mentor engineers across org boundaries through technical leadership and collaboration Knowledge, Skills & Abilities: Strong understanding of machine learning and deep learning approaches and algorithms, and their applications to advertising, recommendation, and/or search domain Experience working for a team whose primary output is online ranking / recommendation models Ability to design, train, and optimize advanced machine learning models Ability to proactively learn new concepts and technology and apply them at work Skilled at solving open ambiguous problems Strong collaboration and mentorship skills Minimum Qualifications: Preferred Qualifications: Experience in online advertising, including ad targeting, ranking, auction, and/or marketplace optimization. Advanced degree in computer science or related field Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks Experience working with machine learning, ranking infrastructures, and system design If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information . "Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable). Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! Compensation In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future. Zone A (CA, WA, NYC) : The base salary range for this position is $229,000-$343,000 annually. Zone B : The base salary range for this position is $218,000-$326,000 annually. Zone C : The base salary range for this position is $195,000-$292,000 annually. This position is eligible for equity in the form of RSUs.

• Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition • Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline • Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments • Use LLMs and generative AI to accelerate internal ML workflows — synthetic data generation, code generation, automated analysis, and rapid prototyping • Use simulation to de-risk ML model deployments — validate new bidding and optimization strategies before they touch live traffic • Define the technical direction for simulation and AI infrastructure and mentor engineers on the team

• Write production Python that powers real-time bidding, model training, and campaign optimization • Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second • Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend • Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution • Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems • Serve as a technical lead and mentor on a distributed engineering team