Powering Pinterest Ads Analytics with Apache Druid

2,011
Pinterest
Pinterest's profile on StackShare is not actively maintained, so the information here may be out of date.

The Change

When we launched Promoted Pins in 2014, we chose Apache HBase as our database to store and serve all of our reporting metrics. At the beginning of our ads business, this was an appropriate choice because the number of reporting features needed and overall traffic was low. Additionally, HBase already had a proven record in the industry at this time, and we knew how to successfully operate an HBase cluster.

Five years later, our business has matured. As our ads scale has increased dramatically, so have the complexities of the metrics we report to our partners, which has rendered HBase insufficient for our fine-grained analytical needs. As a result, we surveyed the available options and settled on Druid to be the core component of our next iteration.

Why Druid?

HBase works very well when it comes to accessing random data points, but it’s not built for fast grouping and aggregation. In the past, we’ve solved this by pre-building these data views, but as the features needed for our reporting expanded, it was no longer possible to store so many different cuts. Druid allowed us to bypass all of this complicated data slicing ingestion logic, and also supports:

  • Real-time ingestion via Kafka
  • Automatic versioning of ingested data
  • Data pre-aggregation based on user-set granularity
  • Approximate algorithms for count-distinct questions
  • A SQL interface
  • Easy to understand code and a very supportive community

Data Ingestion

Druid supports two modes of ingestion: Native and Hadoop. Both are launched via a REST call to the Overlord node. In the native ingestion case, a thread is spawned directly on the MiddleManager node to read input data, while in the Hadoop case, Druid launches a MapReduce job to read the input in parallel. In both cases, the ingested data is automatically versioned based on its output datasource (table) and time interval. Druid will automatically start serving the newest version of the data as soon as it is available and keep the older segments in a disabled state, should we ever need to revert to a previous version. Since we have several different data pipelines producing different sets of metrics with the same dimensions into a single datasource, this was a problem for us. How do we keep the data versioned but not have each independent pipeline overwrite the previous one’s output?

Namespacing shard specs proved to be the answer. Druid’s standard approach to versioning segments is by their datasource name, time interval and time written. We expanded on this system by also including a namespace identifier. We then built a separate versioned interval timeline per namespace in a datasource, rather than just one timeline per datasource:

This also meant that we needed to either change the existing ingestion mechanisms to create segments with namespaces or invent a new ingestion mechanism. Since we ingest billions of events per day, native ingestion is too slow for us, and we were not keen on setting up a new Hadoop cluster and changing the Hadoop indexing code to adhere to namespaces.

Instead, we chose to adapt the metamx/druid-spark-batch project to write our own data ingestion using Spark. The original druid-spark-batch project works in a similar fashion to the Hadoop indexer, but instead of launching a Hadoop job, it launches a Spark job. Our project runs inside of a stand-alone job without the need to use any resources of the Druid cluster at all. It works as follows:

  1. Filter out events not belonging to the output interval
  2. Partition data into intervals based on the configured granularity and number of rows per segment file
  3. Use a pool of Druid’s IncrementalIndex classes to persist intermediate index files on disk in parallel
  4. Use a final merge pass to collect all index files into a segment file
  5. Push to deep storage
  6. Construct and write metadata to MySQL

Once the metadata is written, the Druid coordinator will find new segments on its next pull of the metadata table and assign the new segments to be served by historical nodes.

Cluster Setup

In general, the date ranges for querying advertising data fall into three categories:

  1. Most recent time period to display
  2. Year-over-year performance reporting
  3. Random ad-hoc queries of old, historical data.

The number of queries for the most recent day vastly outnumber all other reporting types. With this understanding, we bucketed our Druid cluster into three historical tiers:

  • A “hot” tier serving the most recent data on expensive compute-optimized nodes to handle large QPS.
  • A “cold” tier on mid compute, lots of disk space-optimized nodes. Serves the last year of data sans data in the Hot tier.
  • An “icy” tier on low compute nodes having even more disk space. Serves all other historical data.

Each historical in the hot tier has very low maximum data capacity to guarantee that all segments the node is serving are loaded in memory without needing to page swap. This ensures low latency for most of our user-driven queries. Queries for older data are generally made by automated systems or report exports which allow for higher latency in preference to high operating cost.

While this works very well for the average query patterns, there are cases of unexpected high load which require higher QPS tolerance from the cluster. The obvious solution here would be to scale up the number of historical nodes for these specific cases, but Druid’s data rebalancing algorithm is very slow at scale. It can take many hours or even days for a multi-terabyte cluster to rebalance data evenly once a new set of servers joins the fleet. To build an efficient auto-scaling solution, we could not afford to wait so long.

Since optimizing the rebalancing algorithm would be very risky to deploy on a huge production system, we decided instead to implement a solution for mirroring tiers. This system uses maximum bipartite matching to link each node in the mirror tier to exactly one node in the primary tier. Once the link is established, the mirroring historical doesn’t need to wait to be assigned segments by the rebalancing algorithm. Instead, it will pull the list of segments served by the linked node from the primary tier and download those from deep storage for serving. It doesn’t need to worry about replication since we expect these mirror tiers to be turned on and off very frequently, operating only during periods of heavy traffic. See below for more information:

During testing we were able to achieve significant auto-scaling improvement given a mirroring tier solution. The most significant portion of time taken now from server launch to query serving is limited I/O bandwidth from deep storage.

Time taken to load 31 TB of data. 2 hours for natural rebalancing. 5 minutes for mirroring tier.

Query Construction

Our Druid deployment is external facing, powering queries made interactively from our ads management system as well as programmatically through our external APIs. Often these query patterns will look very different per use case, but in all cases, we needed a service to construct Druid queries quickly and efficiently as well as to reject any invalid queries. Programmatic access to our API means that we receive a fair number of queries which request invalid dates or repetitive queries asking for entities which have no metrics.

Percent of queries returning empty results per API client. Some clients request non-existent metrics up to 90% of the time.

Constructing and asking Druid to execute these queries is possible but accrues overhead which is unaffordable in a low-latency system. To short-circuit queries for non-existent entities, we developed a metadata store listing entities and their metric-containing time intervals. If a query’s requested entities have no metrics for the specified time intervals, we can return immediately and relieve Druid from additional network and CPU workload.

Druid supports two APIs to query data: native and SQL. SQL support is a newer feature backed by Apache Calcite. In the backend, it takes a Druid SQL query, parses it, analyzes it, and turns it into a Druid native query which is then executed. SQL support has numerous advantages — it’s much more user friendly and certainly better at constructing more efficient ad-hoc queries than if the user was to come up with some unfamiliar JSON.

SQL was our first choice when implementing our query constructor and execution service namely due to our familiarity with SQL. It worked, but we quickly identified certain query patterns which Druid could not complete and traced the issue to performance bottlenecks in the SQL parser for queries with thousands of filters or many complicated projections. In the end, we settled on using native queries as our primary access path to Druid, keeping SQL support for internal use cases that are not latency sensitive.

System Tuning

Coming from a key-value world, the individual queries originating from our API layer were tailored to be low in complexity to allow an optimal number of point lookups. This also meant querying each entity individually, resulting in high QPS in the backend. To minimize the disruption to our entire infrastructure, we wanted to keep our changes simple and get as close as possible to simply exchanging HBase for Druid. In practice, that proved to be completely impossible.

Druid holds network connections between servers in a greedy manner, using a set of new connections per query. It also opens object handles per query, which is the primary bottleneck in a high QPS system. To lessen the network load, we ramped up the complexity of each query by batching the number of requested entities. We observed our system to perform at its best with between 1,000 to 2,000 requested entities in IN filter type queries, although every deployment will differ.

QPS after implementing query batching. 15,000 request / second peaks lowered by 10x

On the server side, we found the basic cluster tuning guidance suggested by the Druid documentation very helpful. One non-obvious caveat is being mindful of how many GroupBy queries can be executed at any time given the number of merge buffers configured. GroupBy queries should be avoided whenever possible in preference to Timeseries and TopN queries. These types of queries do not require merge buffers and therefore need fewer resources to execute. In our stack, we have the option to impose rate limiting based on query type to avoid too many GroupBy queries at once given the number of configured merge buffers.

The Future

We’re excited to have finished the long journey to bring Druid into production, but of course our work continues. As Pinterest’s business grows, our work on the core Druid platform for analytics has to evolve alongside it. It might be difficult to seamlessly contribute all our effort into the main Druid repository, but we hope to share our effort with the community. Namely on features such as a Spark writer and reader of Druid segments, mirroring tiers for auto scaling, and developing a new multiplexing IPC protocol instead of HTTP. While ads analytics matures, we are also onboarding other teams’ use cases, helping them discover how best to use Druid at scale for their needs.

Acknowledgments

This project was a joint effort across multiple teams: Ads Data, Ads API, and Storage & Caching. Contributors and advisors include Lucilla Chalmer, Tian-Ying Chang, Julian Jaffe, Eric Nguyen, Jian Wang, Weihong Wang, Caijie Zhang, and Wayne Zhao.

Credit also goes to Imply.io leaders Gian Merlino and Fangjin Yang for introducing us to and helping us bootstrap Druid.

We’re building the world’s first visual discovery engine. More than 320 million people around the world use Pinterest to dream about, plan and prepare for things they want to do in life. Come join us!

Pinterest
Pinterest's profile on StackShare is not actively maintained, so the information here may be out of date.
Tools mentioned in article
Open jobs at Pinterest
Sr. Staff Software Engineer, Ads ML I...
San Francisco, CA, US; , CA, US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>Pinterest is one of the fastest growing online advertising platforms. Continued success depends on the machine-learning systems, which crunch thousands of signals in a few hundred milliseconds, to identify the most relevant ads to show to pinners. You’ll join a talented team with high impact, which designs high-performance and efficient ML systems, in order to power the most critical and revenue-generating models at Pinterest.</p> <p><strong>What you’ll do</strong></p> <ul> <li>Being the technical leader of the Ads ML foundation evolution movement to 2x Pinterest revenue and 5x ad performance in next 3 years.</li> <li>Opportunities to use cutting edge ML technologies including GPU and LLMs to empower 100x bigger models in next 3 years.&nbsp;</li> <li>Tons of ambiguous problems and you will be tasked with building 0 to 1 solutions for all of them.</li> </ul> <p><strong>What we’re looking for:</strong></p> <ul> <li>BS (or higher) degree in Computer Science, or a related field.</li> <li>10+ years of relevant industry experience in leading the design of large scale &amp; production ML infra systems.</li> <li>Deep knowledge with at least one state-of-art programming language (Java, C++, Python).&nbsp;</li> <li>Deep knowledge with building distributed systems or recommendation infrastructure</li> <li>Hands-on experience with at least one modeling framework (Pytorch or Tensorflow).&nbsp;</li> <li>Hands-on experience with model / hardware accelerator libraries (Cuda, Quantization)</li> <li>Strong communicator and collaborative team player.</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$135,150</span><span class="divider">&mdash;</span><span>$278,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
Senior Staff Machine Learning Enginee...
San Francisco, CA, US; , CA, US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>We are looking for a highly motivated and experienced Machine Learning Engineer to join our team and help us shape the future of machine learning at Pinterest. In this role, you will tackle new challenges in machine learning that will have a real impact on the way people discover and interact with the world around them.&nbsp; You will collaborate with a world-class team of research scientists and engineers to develop new machine learning algorithms, systems, and applications that will bring step-function impact to the business metrics (recent publications <a href="https://arxiv.org/abs/2205.04507">1</a>, <a href="https://dl.acm.org/doi/abs/10.1145/3523227.3547394">2</a>, <a href="https://arxiv.org/abs/2306.00248">3</a>).&nbsp; You will also have the opportunity to work on a variety of exciting projects in the following areas:&nbsp;</p> <ul> <li>representation learning</li> <li>recommender systems</li> <li>graph neural network</li> <li>natural language processing (NLP)</li> <li>inclusive AI</li> <li>reinforcement learning</li> <li>user modeling</li> </ul> <p>You will also have the opportunity to mentor junior researchers and collaborate with external researchers on cutting-edge projects.&nbsp;&nbsp;</p> <p><strong>What you'll do:&nbsp;</strong></p> <ul> <li>Lead cutting-edge research in machine learning and collaborate with other engineering teams to adopt the innovations into Pinterest problems</li> <li>Collect, analyze, and synthesize findings from data and build intelligent data-driven model</li> <li>Scope and independently solve moderately complex problems; write clean, efficient, and sustainable code</li> <li>Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across growth, discovery, ads and search</li> </ul> <p><strong>What we're looking for:</strong></p> <ul> <li>Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Pytorch, Tensorflow, MLFlow)</li> <li>Experience in research and in solving analytical problems</li> <li>Strong communicator and team player. Being able to find solutions for open-ended problems</li> <li>8+ years working experience in the r&amp;d or engineering teams that build large-scale ML-driven projects</li> <li>3+ years experience leading cross-team engineering efforts that improves user experience in products</li> <li>MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field</li> </ul> <p><strong>Desired skills:</strong></p> <ul> <li>Strong publication track record and industry experience in shipping machine learning solutions for large-scale challenges&nbsp;</li> <li>Cross-functional collaborator and strong communicator</li> <li>Comfortable solving ambiguous problems and adapting to a dynamic environment</li> </ul> <p>This position is not eligible for relocation assistance.</p> <p>#LI-SA1</p> <p>#LI-REMOTE</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$158,950</span><span class="divider">&mdash;</span><span>$327,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
Staff Software Engineer, ML Training
San Francisco, CA, US; , CA, US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>The ML Platform team provides foundational tools and infrastructure used by hundreds of ML engineers across Pinterest, including recommendations, ads, visual search, growth/notifications, trust and safety. We aim to ensure that ML systems are healthy (production-grade quality) and fast (for modelers to iterate upon).</p> <p>We are seeking a highly skilled and experienced Staff Software Engineer to join our ML Training Infrastructure team and lead the technical strategy. The ML Training Infrastructure team builds platforms and tools for large-scale training and inference, model lifecycle management, and deployment of models across Pinterest. ML workloads are increasingly large, complex, interdependent and the efficient use of ML accelerators is critical to our success. We work on various efforts related to adoption, efficiency, performance, algorithms, UX and core infrastructure to enable the scheduling of ML workloads.</p> <p>You’ll be part of the ML Platform team in Data Engineering, which aims to ensure healthy and fast ML in all of the 40+ ML use cases across Pinterest.</p> <p><strong>What you’ll do:</strong></p> <ul> <li>Implement cost effective and scalable solutions to allow ML engineers to scale their ML training and inference workloads on compute platforms like Kubernetes.</li> <li>Lead and contribute to key projects; rolling out GPU sharing via MIGs and MPS , intelligent resource management, capacity planning, fault tolerant training.</li> <li>Lead the technical strategy and set the multi-year roadmap for ML Training Infrastructure that includes ML Compute and ML Developer frameworks like PyTorch, Ray and Jupyter.</li> <li>Collaborate with internal clients, ML engineers, and data scientists to address their concerns regarding ML development velocity and enable the successful implementation of customer use cases.</li> <li>Forge strong partnerships with tech leaders in the Data and Infra organizations to develop a comprehensive technical roadmap that spans across multiple teams.</li> <li>Mentor engineers within the team and demonstrate technical leadership.</li> </ul> <p><strong>What we’re looking for:</strong></p> <ul> <li>7+ years of experience in software engineering and machine learning, with a focus on building and maintaining ML infrastructure or Batch Compute infrastructure like YARN/Kubernetes/Mesos.</li> <li>Technical leadership experience, devising multi-quarter technical strategies and driving them to success.</li> <li>Strong understanding of High Performance Computing and/or and parallel computing.</li> <li>Ability to drive cross-team projects; Ability to understand our internal customers (ML practitioners and Data Scientists), their common usage patterns and pain points.</li> <li>Strong experience in Python and/or experience with other programming languages such as C++ and Java.</li> <li>Experience with GPU programming, containerization, orchestration technologies is a plus.</li> <li>Bonus point for experience working with cloud data processing technologies (Apache Spark, Ray, Dask, Flink, etc.) and ML frameworks such as PyTorch.</li> </ul> <p>This position is not eligible for relocation assistance.</p> <p>#LI-REMOTE</p> <p><span data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;#LI-AH2&quot;}" data-sheets-userformat="{&quot;2&quot;:14464,&quot;10&quot;:2,&quot;14&quot;:{&quot;1&quot;:2,&quot;2&quot;:0},&quot;15&quot;:&quot;Helvetica Neue&quot;,&quot;16&quot;:12}">#LI-AH2</span></p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$135,150</span><span class="divider">&mdash;</span><span>$278,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
Distinguished Engineer, Frontend
San Francisco, CA, US; , US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>As a Distinguished Engineer at Pinterest, you will play a pivotal role in shaping the technical direction of our platform, driving innovation, and providing leadership to our engineering teams. You'll be at the forefront of developing cutting-edge solutions that impact millions of users.</p> <p><strong>What you’ll do:</strong></p> <ul> <li>Advise executive leadership on highly complex, multi-faceted aspects of the business, with technological and cross-organizational impact.</li> <li>Serve as a technical mentor and role model for engineering teams, fostering a culture of excellence.</li> <li>Develop cutting-edge innovations with global impact on the business and anticipate future technological opportunities.</li> <li>Serve as strategist to translate ideas and innovations into outcomes, influencing and driving objectives across Pinterest.</li> <li>Embed systems and processes that develop and connect teams across Pinterest to harness the diversity of thought, experience, and backgrounds of Pinployees.</li> <li>Integrate velocity within Pinterest; mobilizing the organization by removing obstacles and enabling teams to focus on achieving results for the most important initiatives.</li> </ul> <p>&nbsp;<strong>What we’re looking for:</strong>:</p> <ul> <li>Proven experience as a distinguished engineer, fellow, or similar role in a technology company.</li> <li>Recognized as a pioneer and renowned technical authority within the industry, often globally, requiring comprehensive expertise in leading-edge theories and technologies.</li> <li>Deep technical expertise and thought leadership that helps accelerate adoption of the very best engineering practices, while maintaining knowledge on industry innovations, trends and practices.</li> <li>Ability to effectively communicate with and influence key stakeholders across the company, at all levels of the organization.</li> <li>Experience partnering with cross-functional project teams on initiatives with significant global impact.</li> <li>Outstanding problem-solving and analytical skills.</li> </ul> <p>&nbsp;</p> <p>This position is not eligible for relocation assistance.</p> <p>&nbsp;</p> <p>#LI-REMOTE</p> <p>#LI-NB1</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$242,029</span><span class="divider">&mdash;</span><span>$498,321 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
You may also like