Zigsaw

Revenue Operations

• Identify and champion automation and AI/ML opportunities to reduce manual effort and improve accuracy across the order lifecycle.
• Partner with Sales Strategy, Revenue Operations, and Sales to improve order experience and policy clarity, while driving prioritized process and automation enhancements that boost efficiency, quality, and compliance.
• Drive data-driven accountability and strategic insights by developing, monitoring, and analyzing key operational metrics to identify performance trends, optimize processes, and enable informed decision-making across cross-functional teams.
• Lead change management efforts for system implementations, process redesigns, and organizational transitions.
• Define and execute the long-term vision and roadmap for Order Management and Quote-to-Cash operations, aligning with broader Finance and Go-to-Market strategy.
• Own the end-to-end order lifecycle, delivering accurate and timely orders.
• Lead, mentor, and develop a high-performing, globally distributed team with clear goals, regular coaching/reviews, and a culture of accountability and excellence.
• Design and run case and queue management to consistently meet or exceed SLAs and customer expectations.
• Serve as the point of escalation for complex orders, exceptions, and policy approvals; drive root-cause fixes to prevent recurrence.
• Establish and maintain SOPs, playbooks, and training; ensure compliance with accounting policy, revenue recognition readiness, and internal controls.
• Standardize reporting to measure team performance and inform decisions; evaluate and share key insights regularly with executive leadership.
• Scale operations to support business growth, including global expansion, new product launches, and evolving monetization models.

Manager, Revenue Operations, Order Management

AI Engineer

• As a Staff Technical Program Manager focusing on cross-engineering special projects, you will identify, scope, champion, and land key strategic projects important to advancing Pinterest’s platform and underpinning.
• Identify, scope, champion, and deliver high-impact, cross-engineering initiatives critical to advancing Pinterest's ML/AI Platform.
• Use GenAI as the default operating model for program execution.
• Leverage AI coding assistants to build lightweight solutions that improve decision-making.
• Follow Pinterest's AI guidance for risk, governance, and compliance.
• Partner closely with technical domain owners across MLP, Agent Platform (Helix), Core Infra, and Ads.
• Convert strategic platform priorities into structured, milestone-driven execution plans.
• Serve as a thought leader to cross-functional stakeholders during annual and quarterly planning cycles.
• Define, institutionalize, and continuously improve program management processes, frameworks, and tooling.
• Build transparent, adaptive communication channels across Engineering, ML Research, Data Science, Product, Security, and Legal.
• Take full accountability for the success of high-impact programs.

Staff Technical Program Manager – ML/AI Platform

Recruitment

• Oversee recruiting for multiple domains (Product Design, Qualitative & Quantitative Researcher’s, Content Designers, Technical Program Managers, IT/Operations), spanning different levels across those teams, leveraging innovative sourcing strategies to attract top talent and ensure hiring goals are met.
• Develop and execute comprehensive sourcing plans, including talent pooling, market mapping, and competitor analysis.
• Collaborate with hiring managers, key stakeholders (e.g., HR, Compensation, etc), Sourcing partners to align recruiting strategies with organizational goals.
• Manage pipeline activity, maintain data integrity, and provide data-driven updates to stakeholders to inform decisions.
• Lead initiatives to improve and scale recruiting operations, including onboarding and training recruiters, setting performance expectations, and driving hiring strategies.
• Drive the offer process, including negotiations and closing candidates, while managing team goals and performance outcomes.
• At Pinterest our Recruiters and Sourcers use AI to build and refine their sourcing strategies. You’ll be expected to use AI to:
• Translate hiring manager needs into clear, actionable search strategies.
• Understand the talent landscape and map companies, teams, talent clusters, and candidate movement.
• Craft compelling, personalized, and relevant outreach that improves response rates - while keeping communications human, inclusive, and role-relevant.

Recruiting Lead

People Operations

• Partner with stakeholders across the organization to deliver high-impact, scalable HR technology solutions for projects spanning HCM, benefits, compensation, talent, performance, absence, and more.
• Analyze business processes and needs, translating them into innovative, efficient solutions through requirements gathering, solution design, and implementation.
• Assess Workday’s HR technology ecosystem to identify, recommend, and integrate advanced SaaS and cloud-based solutions—such as Workday Extend, Prism, and workflow automation tools—to streamline HR processes and improve employee experiences and operational efficiency.
• Champion the adoption of AI technologies by equipping the team with best practices and fostering a culture of experimentation to drive innovation in People processes.
• Enhance Workday system performance and data quality by identifying and responsibly integrating AI-powered solutions that streamline HR operations and deliver measurable impact across the employee lifecycle at Pinterest.
• Lead end-to-end delivery of HR transformation projects, ensuring effective integration, high user adoption, and alignment with organizational goals. Drive best practices in project management, documentation, testing, change management, and training.
• Develop and deliver comprehensive training materials and advanced support to empower HR partners and drive successful technology adoption.
• Promote technology governance, compliance (including data privacy and protection), and effective vendor management.

Senior People Tech Specialist

• Create close working relationships with business stakeholders within and outside of the People team to be their go to technology analyst - you’ll act as a close partner, advising them to evaluate and implement solutions.
• Evaluate existing technology and new requests, review them against our shared roadmap and make recommendations about scope, timing and solution scalability.
• Manage all the practical implementation tasks including driving requirement definition, solution design, config, testing and implementation with project timelines.
• Use AI tools to streamline daily support tasks in Workday and People systems—experimenting to identify where AI brings the most value, improving the quality and accuracy of ticket triage, data checks, and routine troubleshooting, and documenting effective prompts and process enhancements for team use.
• Apply AI thoughtfully to reporting, documentation, and workflow optimizations—validating outputs for accuracy and usefulness before broader adoption, and sharing new approaches or templates to enable successful practices to be scaled across the team.
• Keep good project hygiene practices across your portfolio of work - your project plans, solution design, requirements,comms and artifacts should be accurate and organized.
• You may also work with other internal stakeholders and IT teams to deliver enhancements, support annual processes, and implement new technology in support of Pinterest.
• As our team grows, you’ll also partner closely with supporting analysts, to whom you will provide direction and lead through project delivery.

People Tech Specialist II

Full-stack Engineer

• Design and implement robust data infrastructure in AWS, using Spark with Scala
• Evolve our core data pipelines to efficiently scale for our massive growth
• Store data in optimal engines and formats, matching your designs to our performance needs and cost factors
• Collaborate with our cross-functional teams to design data solutions that meet business needs
• Design and implement knowledge graphs, exposing their functionality both via Batch Processing and APIs
• Leverage and optimize AWS resources while designing for scale
• Collaborate closely with our Data Science and Product teams

Software Engineer II, Big Data

Technical Program Manager

• Establish and own relationships with key partners across Search teams as their strategic and execution partner for delivering high impact programs.
• Define and drive Search, Relevance, and Merchant ML/AI programs based on current and emerging needs, ensuring scope, responsibilities, and ownership are well-established, risks are proactively identified and mitigated and that programs are executed and landed successfully.
• Lead the day-to-day program management of multiple work streams across the product process, including product strategy, technical design, and experimentation.
• Analyze merchant distribution and shopping experiences in partnership with Data Science; leverage these insights to lead strategy steering committees with cross-functional VPs.
• Build a subject-matter-expertise on query understanding and search engines.
• Drive product, business, and OKR requirements while also streamlining engineering efforts and cost.
• Identify gaps in shopping and search distribution strategy (i.e. signals, content, etc.) and operationalize processes to close those gaps.
• Own and scale the headcount and roadmap planning process for a large engineering org.
• Drive High-Ambiguity strategic bets: Act as the core delivery powerhouse for high-impact, fast-moving experiments. Translate ambitious executive visions into highly executable, phased project plans.
• Manage the overarching portfolio of programs within the org and mentor junior TPMs to optimize resource allocation and uplevel team performance.
• Use GenAI as the default operating model for EP PgM execution—producing AI-assisted first drafts of core program artifacts, modernizing high-toil workflows into AI-first mechanisms (e.g., intake triage, status synthesis, action/decision extraction, risk & dependency tracking), and synthesizing signals to proactively surface risks, decision/trade-offs, and escalation paths.
• Prototype solutions to augment decisions through data (e.g. dashboards, data analysis) or simplify processes (e.g. process and workflow helpers, or internal tools) using AI coding assistants (“vibe coding”).
• Follow Pinterest AI guidance for risk, governance, and safety-by-design: appropriately handle sensitive data, validate AI-generated outputs, document assumptions/limits, and ensure AI-assisted workflows meet applicable policy/compliance expectations before broad adoption.

Staff Technical Program Manager, Search Relevance

Software Engineer

• Lead and grow a cross-functional engineering team working across user-facing product areas and the systems that support them.
• Set clear goals and drive execution against a roadmap aligned with broader product and business priorities.
• Partner with Product, Design, Research, and Data Science to shape strategy, prioritize opportunities, and deliver measurable outcomes.
• Create a strong team environment with clear expectations, healthy operating rhythms, high standards, and psychological safety.
• Coach and develop engineers across levels, helping them grow in scope, impact, and career progression.
• Support technical and architectural decision-making to ensure the team builds reliable, scalable, and maintainable systems.
• Use data, experimentation, and user insight to guide product and engineering decisions.
• Use AI to accelerate analysis, research, drafting, synthesis, and iteration, while applying strong judgment and verification to ensure quality and correctness.

Manager II, Engineering

Machine Learning Engineer

• Lead ML Strategy and Innovation for Foundational Content and User Understanding
• Lead Execution and Deployment Across Pinterest to Deliver Topline Impact
• Build and Scale a World-Class ML Organization
• Drive ML Influence Across Pinterest

Director, Machine Learning Engineering – Content & User Understanding

• Define and execute the observability roadmap, treating it as a product. Understand engineering team needs and translate them into technical solutions with measurable impact
• Architect, build, and scale distributed observability infrastructure (metrics, logs, traces) to handle massive volumes across Pinterest's distributed systems
• Build high-performance data pipelines and storage for real-time and historical telemetry analysis at Pinterest scale
• Champion Best Practices: Establish observability standards and patterns across the organization, making it easy for teams to instrument their services and gain actionable insights
• Technical Leadership: Mentor engineers, lead architectural reviews, and influence technical decisions across teams to improve overall system reliability and performance
• Cross-functional Collaboration: Partner with SRE, Infrastructure, Product Engineering, and other teams to understand pain points and deliver solutions that improve developer productivity and system reliability
• Innovation: Stay current with observability trends and technologies, evaluating and adopting cutting-edge tools and techniques to keep Pinterest at the forefront

Staff Software Engineer, Observability

Product Manager

• Own and drive the Signal Lifecycle product roadmap, including ML Flywheel infrastructure, auto-deployment, model onboarding, golden dataset management, and signal performance measurement
• Define and ship ML Signal Management — a unified backbone that elevates ML signals into first-class entities with comprehensive metadata, cross-system naming, and API access
• Partner with ML Engineering to reduce model iteration time through automated retraining, evaluation, and deployment pipelines
• Own measurement infrastructure — golden dataset strategy, prevalence measurement, model performance dashboards, and experimentation frameworks
• Lead cross-functional signal strategy with Content Safety, Enforcement Systems, Data Science, and Operations

Staff Product Manager, Model Lifecycle & Management

• Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.
• Partner with DS teams to develop a content ecosystem measurement framework to quantify content health and performance (e.g., content quality, freshness, diversity, coverage, creator/content sustainability, and user value), and align it with company/business goals.
• Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.
• Deeply understand what content works and why by combining causal thinking, experimentation, and model interpretability to connect content attributes and distribution mechanisms to downstream user and business outcomes.
• Build and optimize content marketplace mechanisms that balance multi-sided incentives and constraints (e.g., users, creators/publishers, advertisers, internal policy/safety), while maximizing long-term ecosystem value.
• Design multi-objective optimization approaches that manage tradeoffs across relevance, quality, diversity, creator incentives, integrity/safety, and monetization.
• Partner closely with cross-functional teams (Product, Data Science, UX Research, Content/Creator teams, Trust & Safety, Ads, Infra) to translate ambiguous ecosystem problems into clear technical roadmaps and deliver measurable impact.
• Mentor and grow junior ML engineers through technical coaching, design reviews, career development support, and creating a culture of strong engineering and scientific rigor.
• Raise the quality bar for ML engineering by establishing best practices for data quality, model governance, reliability, privacy-aware design, and operational excellence.
• Communicate clearly and influence broadly by producing crisp technical proposals, aligning stakeholders on tradeoffs, and driving decisions across org boundaries.
• Explore and apply advanced methods where beneficial—e.g., game-theoretic approaches, reinforcement learning, mechanism design, or bandit-style optimization—to improve marketplace dynamics and long-term ecosystem outcomes.

Senior Staff Machine Learning Engineer, Content Ecosystem

Affiliate Manager

• Become an expert at Pinterest campaign best practices
• Understand complex processes and products, distilling them down for a less technical audience
• Enable and mobilize sellers to pitch new performance products
• Provide strategic thought leadership to top tier clients
• Identify key issues facing clients and develop proposed solutions
• Work with your region to adapt best practices

Senior Performance Solutions Partner

• Own and drive the T&S Tools product roadmap, including review queue systems (PinQueue), entity inspection and management (Lookup), bulk operations, and domain management tools
• Define and ship AI-native tool capabilities — MCP integrations, AI-assisted workflows, intelligent routing, model testing interfaces, and new experiences that fundamentally change how practitioners interact with T&S systems
• Partner deeply with T&S practitioners — ops reviewers, ML engineers, risk analysts, policy teams — to understand workflows, identify friction, and deliver improvements that compound over time
• Manage the internal customer experience across a diverse user base with different needs and technical depth
• Triage inbound requests from cross-functional stakeholders, communicate priorities, and maintain roadmap focus

Product Manager II, Trust & Safety Tools

• Own a portfolio of personalization programs that span recommendation platforms, ranking and optimization strategies, and new anticipatory experiences (e.g., unified landing pages, intelligent/AI-generated boards), with clear goals, milestones, and success metrics.
• Drive cross-surface execution so that personalization improvements can scale efficiently across multiple experiences (e.g., Homefeed, Explore, SEO landing pages, board and shopping surfaces), working closely with product and engineering owners in each area.
• Partner with Relevance and Data Science to ensure our ranking and modeling work prioritizes long-term value and revisitation (not just short-term clicks), and that experiment learnings systematically inform future roadmap and investment decisions.
• Design and run the operating model for personalization—creating cadences, dashboards, and decision forums that give leaders and teams a clear view of program health, risks, dependencies, and tradeoffs across the personalization portfolio.
• Align stakeholders across organizations (Relevance/ML, product surfaces, infra, curation, growth) when there are competing priorities or ambiguous ownership, and drive crisp, data-backed decisions that keep the personalization roadmap moving.
• Communicate complex work simply, turning deeply technical modeling and platform efforts into clear narratives about impact on Pinners (discovery quality, relevance, anticipatory experiences) and on the business (engagement, retention, revisitation).
• Use GenAI as a default PgM tool—starting from AI-assisted drafts for core artifacts (charters, plans, status, decision memos) and redesigning high-toil PgM workflows (status synthesis, RAID tracking, intake triage) into AI-first mechanisms where it’s safe and effective to do so.
• Prototype lightweight AI-assisted helpers (e.g., scripts, dashboards, workflow tools) with AI coding assistants to make it easier to understand personalization experiments, surface-level performance, and program health—while adhering to Pinterest’s AI governance and safety-by-design expectations.