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PinCompute: A Kubernetes Backed Basic Goal Compute Platform for Pinterest | by Pinterest Engineering | Pinterest Engineering Weblog | Oct, 2023


Harry Zhang, Jiajun Wang, Yi Li, Shunyao Li, Ming Zong, Haniel Martino, Cathy Lu, Quentin Miao, Hao Jiang, James Wen, David Westbrook | Cloud Runtime Workforce

Picture Supply: https://unsplash.com/images/ZfVyuV8l7WU

Trendy compute platforms are foundational to accelerating innovation and operating functions extra effectively. At Pinterest, we’re evolving our compute platform to supply an application-centric and absolutely managed compute API for the ninetieth percentile of use instances. This may speed up innovation by way of platform agility, scalability, and a decreased price of retaining techniques updated, and can enhance effectivity by operating our customers’ functions on Kubernetes-based compute. We seek advice from this subsequent era compute platform as PinCompute, and our multi-year imaginative and prescient is for PinCompute to run probably the most mission crucial functions and providers at Pinterest.

PinCompute aligns with the Platform as a Service (PaaS) cloud computing mannequin, in that it abstracts away the undifferentiated heavy lifting of managing infrastructure and Kubernetes and allows customers to deal with the distinctive points of their functions. PinCompute evolves Pinterest structure with cloud-native ideas, together with containers, microservices, and repair mesh, reduces the price of retaining techniques updated by offering and managing immutable infrastructure, working system upgrades, and graviton cases, and delivers prices financial savings by making use of enhanced scheduling capabilities to giant multi-tenant Kubernetes clusters, together with oversubscription, bin packing, useful resource tiering, and trough utilization.

On this article, we focus on the PinCompute primitives, structure, management airplane and knowledge airplane capabilities, and showcase the worth that PinCompute has delivered for innovation and effectivity at Pinterest.

PinCompute is a regional Platform-as-a-Service (PaaS) that builds on prime of Kubernetes. PinCompute’s structure consists of a bunch Kubernetes cluster (host cluster) and a number of member Kubernetes clusters (member clusters). The host cluster runs the regional federation management airplane, and retains monitor of workloads in that area. The member clusters are zonal, and are used for the precise workload executions. Every zone can have a number of member clusters, which strictly aligns with the failure area outlined by the cloud supplier, and clearly defines fault isolation and operation boundaries for the platform to make sure availability and management blast radius. All member clusters share an ordinary Kubernetes setup throughout management airplane and knowledge airplane capabilities, they usually assist heterogeneous capabilities akin to completely different workload varieties and {hardware} alternatives. PinCompute is multi-tenant, the place a wide range of forms of workloads from completely different groups and organizations share the identical platform. The platform offers needful isolations to make sure it may be shared throughout tenants securely and effectively.

Determine 1: Excessive Stage Structure of PinCompute

Customers entry the platform by way of Compute APIs to carry out operations on their workloads. We leverage Customized Assets (CR) to outline the sorts of workloads supported by the platform, and the platform presents a variety of workload orchestration capabilities which helps each batch jobs and lengthy operating providers in numerous varieties. When a workload is submitted to the platform, it first will get endured with the host cluster’s Kubernetes API. The federation management airplane will then kick in to carry out workload administration duties wanted on the regional stage, together with quota enforcement, workload sharding, and member cluster choice. Then, the workload shards get propagated to member clusters for execution. The member cluster management airplane consists of a mixture of in-house and open supply operators which can be chargeable for orchestrating workloads of various varieties. The federation management airplane additionally collects execution statuses of workloads from their corresponding member clusters and aggregates them to be consumable by way of PinCompute APIs.

Determine 2: Workflow for Execution and Standing Aggregation of PinCompute
Determine 3: Workload structure on PinCompute

PinCompute primitives serve heterogeneous workloads throughout Pinterest, from lengthy operating, run-to-finish, ML coaching, scheduled run, and extra. These use instances are primarily divided into three classes: (1) basic objective compute and repair deployment, (2) run-to-finish jobs, and (3) infrastructure providers. Pinterest run-to-finish jobs and infrastructure providers are supported by present Kubernetes native and Pinterest-specific sources, and with our newest ideas on easy methods to outline easy, intuitive and extendable compute primitives, PinCompute introduces a brand new set of primitives for basic objective compute and repair deployment. These primitives embrace PinPod, PinApp, and PinScaler.

PinPod is the fundamental constructing block for basic objective compute at Pinterest. Just like the native Kubernetes Pod, PinPod inherits the Pod’s essence of being a foundational constructing block whereas offering further Pinterest-specific capabilities. This contains options like per container updates, managed sidecars, knowledge persistence, failovers, and extra that enable PinPod to be simply leveraged as a constructing block underneath numerous manufacturing situations at Pinterest. PinPod is designed to create a transparent divide between software and infrastructure groups, whereas nonetheless retaining the light-weighted nature of operating containers. It solves many present ache factors: for instance, the per container replace can pace up software rolling updates, cut back useful resource consumption, and get rid of disturbance to consumer containers throughout infra sidecar upgrades.

PinApp is an abstraction that gives one of the best ways to run and handle lengthy operating functions at Pinterest. By leveraging PinPod as an software reproduction, PinApp inherits all of the integrations and greatest practices about software program supply from PinPod. Because of the federation management airplane, PinApp presents a set of built-in orchestration capabilities to meet widespread distributed software administration necessities, which incorporates zone-based rollouts and balancing zonal capability. PinApp helps the performance supplied by Kubernetes native primitives akin to Deployments and ReplicaSets but in addition contains extensions like deployment semantics to fulfill enterprise wants and improve manageability.

PinScaler is an abstraction that helps software auto scaling at Pinterest. It’s built-in with Statsboard, Pinterest’s native metrics dashboard, permitting customers to configure application-level metrics with desired thresholds to set off scaling together with scaling safeguards, akin to a calm down window and reproduction min/max limitations. PinScaler helps easy scaling with CPU and reminiscence metrics, in addition to scheduled scaling and customized metrics to assist numerous manufacturing situations.

Determine 4: PinCompute Primitives: PinPod, PinApp, and PinScaler. PinPod operates as an unbiased workload, and likewise a reusable constructing block for the higher-order primitive PinApp. PinScaler robotically scales PinApp.

Returning to the larger image, PinCompute leverages the subsequent era primitives (PinPod, PinApp, PinScaler), constructing blocks from native Kubernetes and open supply communities, together with deep integrations with federation structure to supply the next classes of use instances:

(1) Basic objective compute and repair deployment: That is dealt with by PinCompute’s new primitive varieties. PinApp and PinScaler assist long-running stateless providers deploy and scale rapidly. PinPod capabilities as a basic objective compute unit and is at present serving Jupyter Pocket book for Pinterest builders.

(2) Run-to-finish jobs: PinterestJobSet leverages Jobs to supply customers a mechanism to execute run-to-finish, framework-less parallel processings; PinterestTrainingJob leverages TFJob and PyTorchJob from the Kubeflow group for distributed coaching; PinterestCronJob leverages CronJob to execute scheduled jobs based mostly on cron expressions.

(3) Infrastructure providers: We have now PinterestDaemon leveraging DaemonSet, and a proprietary PinterestSideCar to assist completely different deploy modes of infrastructure providers. Parts which can be in a position to be shared by a number of tenants (e.g. logging agent, metrics agent, configuration deployment agent) are deployed as PinterestDaemons, which ensures one copy per node, shared by all Pods on that node. These that can not be shared will leverage PinterestSideCar and will likely be deployed as sidecar containers inside consumer Pods.

The PinCompute primitives allow Pinterest builders to delegate infrastructure administration and the related considerations of troubleshooting and operations, permitting them to focus on evolving enterprise logics to raised serve Pinners.

Customers entry PinCompute primitives by way of PinCompute’s Platform Interfaces, which consists of an API layer, a shopper layer for the APIs, and the underlying providers/storages that assist these APIs.

Determine 5: Excessive stage structure of PinCompute Platform Interface layer

PinCompute API

PinCompute API is a gateway for customers to entry the platform. It offers three teams of APIs: workload APIs, operation APIs, and perception APIs. Workload APIs incorporates strategies to carry out CRUD actions on compute workloads, debugging APIs present mechanisms akin to stream logs or open container shells to troubleshoot dwell workloads, and perception APIs present customers with runtime info akin to software state change and system inside occasions to assist customers to grasp the state of their present and previous workloads.

Why PinCompute API

Introducing PinCompute API on prime of uncooked Kubernetes APIs has many advantages. First, as PinCompute federates many Kubernetes clusters, PinCompute API integrates consumer requests with federation and aggregates cross-cluster info to type a holistic user-side view of the compute platform. Second, PinCompute API accesses Kubernetes API effectively. For instance, it incorporates a caching layer to serve learn APIs effectively, which offloads costly checklist and question API calls from Kubernetes API server. Lastly, as a gateway service, PinCompute API ensures uniformed consumer expertise when accessing completely different PinCompute backend providers akin to Kubernetes, node service, insights service, venture governance providers, and so on.

Determine 6: PinCompute API knowledge stream

Integrating With Pinterest Infrastructure

This layer incorporates Pinterest’s infrastructure capabilities like charge limiting and safety practices to simplify the Kubernetes API utilization and supply a steady interface for our API shoppers and builders. The PinCompute API implements charge limiting mechanisms to make sure honest useful resource utilization leveraging our Visitors crew’s charge limiting sidecar, benefiting from reusable Pinterest parts. PinCompute API can also be absolutely built-in with Pinterest’s proprietary safety primitives to make sure authentication, authorization, and auditing to observe paved paths. Such integration allows us to supply Pinterest builders with unified entry management expertise with granularity at API name and API useful resource stage. These integrations are crucial for PinCompute APIs to be dependable, safe, and compliant.

Enhanced API Semantics

PinCompute API offers enhanced API semantics on prime of the Kubernetes API to enhance the consumer expertise. One essential enhancement PinCompute API does is that it presents the uncooked Kubernetes knowledge mannequin in a simplified means with solely info related to constructing software program at Pinterest, which not solely reduces the infrastructure studying curve for builders who deal with constructing excessive stage software logics, but in addition improved knowledge effectivity for API serving. For instance, eradicating managed fields will cut back as much as 50% knowledge dimension for PinCompute API calls. We additionally designed the APIs in a means that’s extra descriptive to be used instances akin to pause, cease, restart-container, and so on., that are intuitive and simple to make use of in lots of situations. PinCompute offers OpenAPI documentation and auto generated purchasers, documentation and SDKs to assist customers self-serve constructing functions on PinCompute.

PinCompute SDK

We strategically put money into constructing an SDK for purchasers to standardize entry to PinCompute. With the SDK, we’re in a position to encapsulate greatest practices akin to error dealing with, retry with backoff, logging, and metrics as reusable constructing blocks, and guarantee these greatest practices are all the time utilized to a shopper. We additionally publish and handle versioned SDKs with clear steering on easy methods to develop on prime of the SDK. We carefully work with our customers to make sure the adoption of the most recent and biggest variations of the SDK for optimized interactions with PinCompute.

Useful resource Mannequin

PinCompute helps three useful resource tiers: Reserved, OnDemand, and Preemptible. Customers outline the useful resource quota of their tasks for every tier. Reserved tier quotas are backed by a fixed-size useful resource pool and a devoted workload scheduling queue, which ensures scheduling throughput and capability availability. OnDemand tier quotas leverage a globally shared, and dynamically sized useful resource pool, serving workloads in a first-come, first-serve method. Preemptible tier is being developed to make opportunistic utilization of unused Reserved tier and OnDemand tier capability, which might get reclaimed when wanted by their corresponding tiers. PinCompute clusters are additionally provisioned with a buffer house consisting of lively however unused sources to accommodate workload bursts. The next diagram illustrates the useful resource mannequin of PinCompute.

Determine 7: PinCompute useful resource mannequin

Scheduling Structure

PinCompute consists of two layers of scheduling mechanisms to make sure efficient workload placements. Cluster stage scheduling is carried out in PinCompute’s regional federation management airplane. Cluster stage scheduling takes a workload and picks a number of member clusters for execution. Throughout cluster stage scheduling, the workload is first handed by way of a gaggle of filters that filter out clusters that can’t match, after which leverage a gaggle of rating calculators to rank candidate clusters. Cluster stage scheduling ensures excessive stage placement technique and sources necessities are glad, and likewise takes elements akin to load distribution, cluster well being, and so on., into consideration to carry out regional optimizations. Node stage scheduling occurs inside member clusters, the place workloads are transformed to Pods by the corresponding operators. After Pods are created, a Pod scheduler is used to position Pods onto nodes for execution. PinCompute’s Pod scheduler leverages Kubernetes’s scheduler framework, with a mixture of upstream and proprietary plugins to make sure the scheduler helps all options out there in open supply Kubernetes, however on the identical time is optimized to PinCompute’s particular necessities.

Determine 8: PinCompute scheduling structure

PinCompute Price Effectivity

Price effectivity is crucial to PinCompute. We have now enacted numerous strategies to efficiently drive down PinCompute infrastructure price with out compromising on the consumer expertise.

We promote multi-tenancy utilization by eliminating pointless useful resource reservation and migrating consumer workloads to the on-demand useful resource pool that’s shared throughout the federated surroundings. We collaborated with main platform customers to smoothen their workload submission sample to keep away from oversubscription in sources. We additionally began a platform-level initiative to change GPU utilization from P4 household cases to the cost-performant alternate options (i.e. G5 household). The next diagram demonstrates the pattern of PinCompute GPU price vs. capability, the place we efficiently decreased price whereas supporting the rising enterprise.

Determine 9: PinCompute GPU price vs. capability

Transferring ahead, there are a number of on-going tasks in PinCompute to additional improve price effectivity. 1) We are going to introduce preemptable workloads to encourage extra versatile useful resource sharing. 2) We are going to improve the platform useful resource tiering and workload queueing mechanisms to make smarter choices with balanced tradeoff on equity and effectivity when scheduling consumer workloads.

Node structure is a crucial house the place we invested closely to make sure functions are in a position to run on a containerized, multi-tenanted surroundings securely, reliably, and effectively.

Determine 10: Excessive stage structure of PinCompute Node and infrastructure integrations

Pod in PinCompute

Pod is designed to isolate tenants on the node. When a Pod is launched, it’s granted its personal community identification, safety principal, and useful resource isolation boundary atomically, that are immutable throughout a Pod’s lifecycle.

When defining containers inside a Pod, customers can specify two lifecycle choices: important container and sidecar container. Most important containers will honor Pod stage restart coverage, whereas sidecar containers are ensured to be out there so long as important containers must run. As well as, customers can allow begin up and termination ordering between sidecar and important containers. Pod in PinCompute additionally helps per container replace, with which containers may be restarted with new spec in a Pod with out requiring the Pod to be terminated and launched once more. Sidecar container lifecycle and per container replace are crucial options for batch job execution reliability, and repair deployment effectivity.

PinCompute has a proprietary networking plugin to assist a wide range of container networking necessities. Host community is reserved for system functions solely. “Bridge Port” assigns a node-local, non-routable IP to Pods that don’t must serve site visitors. For Pods that must serve site visitors, we offer “Routable IP” allotted from a shared community interface, or Pod can request a “Devoted ENI” for full community segmentation. Community sources akin to ENI and IP allocations are holistically managed by way of cloud useful resource management airplane, which ensures administration effectively.

PinCompute helps a wide range of volumes together with EmptyDir, EBS, and EFS. Particularly, now we have a proprietary quantity plugin for logging, which integrates with in-house logging pipelines to make sure environment friendly and dependable log collections.

Integrating With Pinterest Infrastructure

PinCompute node incorporates crucial integration factors between consumer containers and Pinterest’s infrastructure ecosystem, particularly, safety, site visitors, configuration, logging and observability. These capabilities have unbiased management planes which can be orthogonal to PinCompute, and due to this fact are usually not restricted to any “Kubernetes cluster” boundary.

Infrastructure capabilities are deployed in three manners: host-level daemon, sidecar container, or with a twin mode. Daemons are shared by all Pods operating on the node. Logging, metrics, and configuration propagation are deployed as daemons, as they don’t must leverage Pod’s tenancy or keep within the crucial knowledge paths of the functions operating within the Pod. Sidecar containers function inside Pod’s tenancy and are leveraged by capabilities that depend on Pod’s tenancy or want efficiency ensures akin to site visitors and safety.

Person containers work together with infrastructure capabilities akin to logging, configuration, service discovery by way of file system sharing, and capabilities akin to site visitors and metrics by way of networking (native host or unix area socket). Pod, together with the tenancy definition now we have, ensures numerous infrastructure capabilities may be built-in in a safe and efficient method.

Enhanced Operability

PinCompute node has a proprietary node administration system that enhances visibility and operability of nodes. It incorporates node stage probing mechanisms to ship supplementary alerts for node well being which covers areas akin to container runtime, DNS, gadgets, numerous daemons, and so on. These alerts function a node readiness gate to make sure new nodes are schedulable solely in spite of everything capabilities are prepared, and are additionally used throughout software runtime to help automation and debugging. As a part of node high quality of service (QoS), when a node is marked for reserved tier workloads, it will possibly present enhanced QoS administration akin to configuration pre-downloading or container picture cache refresh. Node additionally exposes runtime APIs akin to container shells and dwell log streaming to assist customers troubleshoot their workloads.

Determine 11: PinCompute’s proprietary node administration system

Prioritizing Automation

Automation has a big return on funding with regards to minimizing human error and boosting productiveness. PinCompute integrates a variety of proprietary providers aimed toward streamlining every day operations.

Computerized Remediation

Operators are sometimes troubled with trivial node well being points. PinCompute is supplied to self-remediate these points with an automated remediation service. Well being probes working on the Node Supervisor detect node issues and mark them by way of particular sign annotations. This sign is monitored and interpreted into actions. Then the remediation service executes actions akin to cordoning or terminating. The parts for detection, monitoring, and the remediation service align with ideas of decoupling and extensibility. Moreover, deliberate charge limiting and circuit-breaking mechanisms are established offering a scientific method to node well being administration.

Determine 12: PinCompute Computerized Remediation Structure

Utility Conscious Cluster Rotation

The first operate of the PinCompute Improve service is to facilitate the rotations of Kubernetes clusters in a safe, absolutely automated method whereas adhering to each PinCompute platform SLOs and consumer agreements regarding rotation protocol and swish termination. When processing cluster rotation, considerations vary from the sequence of rotating various kinds of nodes, simultaneous rotations of nodes, nodes rotated in parallel or individually, and the precise timings of node rotations. Such considerations come up from the varied nature of consumer workloads operating on the PinCompute platform. Via the PinCompute Improve service, platform operators can explicitly dictate how they want cluster rotations to be carried out. This configuration permits for a rigorously managed automated development.

Launch PinCompute

Platform Verification

The PinCompute launch pipeline is constituted by 4 phases, every of them being a person federated surroundings. Adjustments are deployed by way of phases and verified earlier than selling. An end-to-end take a look at framework operates repeatedly on PinCompute to authenticate platform accuracy. This framework emulates a real consumer, and capabilities as a continuing canary to supervise the platform’s correctness.

Determine 13: PinCompute Launch Process

Machine Picture (AMI) Administration

PinCompute selectively presents a finite set of node varieties, taking into consideration consumer wants of {hardware} households, manageability and cost-effectiveness. The AMIs chargeable for bootstrapping these nodes fall into three classes: general-purpose AMIs, machine studying centered AMI, and a customizable AMI. The idea of inheriting from a father or mother AMI and configuration simplifies their administration significantly. Every AMI is tagged in accordance with sort and model, they usually make the most of the Improve service to provoke automated deployments.

Operation and Person Dealing with Instruments

In PinCompute, we offer a set of instruments for platform customers and directors to simply function the platform and the workloads operating on it. We constructed a live-debugging system to supply finish customers with UI-based container shells to debug inside their Pods, in addition to stream console logs and file-based logs to grasp the progress of their operating functions. This instrument leverages proprietary node stage APIs to decouple consumer debugging from crucial management paths akin to Kubernetes API and Kubelet, and ensures failure isolation and scalability. Self-service venture administration together with step-by-step tutorials additionally decreased consumer’s overhead to onboard new tasks or make changes of properties of present tasks akin to useful resource quota. PinCompute’s cluster administration system offers an interactive mechanism for modifying cluster attributes which makes it useful to iterate new hardwares or alter capability settings. The straightforward-to-use instrument chains guarantee environment friendly and scalable operations and over the time significantly improved consumer experiences of the platform.

PinCompute is designed to assist the compute necessities at Pinterest scale. Scalability is a posh objective to attain, and to us, every of PinCompute’s Kubernetes cluster is optimized in the direction of a candy spot with 3000 nodes, 120k pods, and 1000 mutating pod operations per minute, with a 25sec P99 workload finish to finish launch latency. These scaling targets are outlined by the necessities of most functions at Pinterest, and are outcomes of balancing throughout a number of elements together with cluster dimension, workload agility, operability, blast radius and effectivity. This scaling goal makes every Kubernetes cluster a stable constructing block for total compute, and PinCompute’s structure can horizontally scale by including extra member clusters to make sure sufficient scalability for the continual progress of PinCompute footprint.

PinCompute defines its SLOs in two varieties: API availability and platform responsiveness. PinCompute ensures 99.9% availability of its crucial workload orchestration associated APIs. PinCompute presents SLO in management airplane reconcile latency which focuses on the latency for the system to take motion. Such latency varies from seconds to 10s seconds based mostly on workload complexity and corresponding enterprise necessities. For reserved tier high quality of service, PinCompute offers SLO for workload finish to finish launch pace, which doesn’t solely deal with platform’s taking motion, but in addition contains how briskly such actions can take impact. These SLOs are essential alerts for platform stage efficiency and availability, and likewise units excessive requirements for platform builders to iterate platform capabilities with prime quality.

Over the previous few years, now we have matured the platform each in its structure in addition to a set of capabilities Pinterest requires. Introducing compute as Platform as a Service (PaaS) has been seen as the largest win for Pinterest builders. An inside analysis confirmed that > 90% use instances with > 60% infrastructure footprint can profit from leveraging a PaaS to iterate their software program. As platform customers, PaaS abstracts away the undifferentiated heavy lifting of proudly owning and managing infrastructure and Kubernetes, and allows them to deal with the distinctive points of their functions. As platform operators, PaaS allows holistic infrastructure administration by way of standardization, which offers alternatives to reinforce effectivity and cut back the price of retaining infrastructure up-to-date. PinCompute embraces “API First” which defines a crisp assist contract and makes the platform programmable and extendable. Furthermore, a stable definition of “tenancy” within the platform establishes clear boundaries throughout use instances and their interactions with infrastructure capabilities, which is crucial to the success of a multi-tenanted platform. Final however not least, by doubling down on automation, we had been in a position to enhance assist response time and cut back crew KTLO and on-call overhead.

There are lots of thrilling alternatives as PinCompute retains rising its footprint in Pinterest. Useful resource administration and effectivity is a giant space we’re engaged on; tasks akin to multi-tenant price attribution, environment friendly bin packing, autoscaling and capability forecast are crucial to assist an environment friendly and accountable infrastructure in Pinterest. Orchestrating stateful functions is each technically difficult and essential to Pinterest enterprise, and whereas PinPod and PinApp are offering stable foundations to orchestrate functions, we’re actively working with stakeholders of stateful techniques on shareable options to enhance operational effectivity and cut back upkeep prices. We additionally acknowledge the significance of use instances having the ability to entry Kubernetes API. As Kubernetes and its communities are actively evolving, it’s a massive profit to observe business developments and undertake business customary practices, and due to this fact we’re actively working with companion groups and distributors to allow extra Pinterest builders to take action. In the meantime, we’re engaged on contributing again to the group, as we imagine a extensively trusted group is the most effective platform to construct a shared understanding, contribute options and enhancements, and share and soak up wins and learnings in manufacturing for the nice of all. Lastly, we’re evaluating alternatives to leverage managed providers to additional offload infrastructure administration to our cloud supplier.

It has been a multi-year effort to evolve PinCompute to allow a number of use instances throughout Pinterest. We’d wish to acknowledge the next groups and people who carefully labored with us in constructing, iterating, productizing, and enhancing PinCompute:

  • ML Platform: Karthik Anantha Padmanabhan, Chia-Wei Chen
  • Workflow Platform: Evan Li, Dinghang Yu
  • On-line Methods: Ping Jin, Zhihuang Chen
  • App Basis: Yen-Wei Liu, Alice Yang
  • Adverts Supply Infra: Huiqing Zhou
  • Visitors Engineering: Scott Beardsley, James Fish, Tian Zhao
  • Observability: Nomy Abbas, Brian Overstreet, Wei Zhu, Kayla Lin
  • Steady Supply Platform: Naga Bharath Kumar Mayakuntla, Trent Robbins, Mitch Goodman
  • Platform Safety: Cedric Staub, Jeremy Krach
  • TPM — Governance and Platforms: Anthony Suarez, Svetlana Vaz Menezes Pereira

To study extra about engineering at Pinterest, try the remainder of our Engineering Weblog and go to our Pinterest Labs web site. To discover and apply to open roles, go to our Careers web page.

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