Indeed Open Source: All Things Open 2019 Speakers

We’re excited to have three Indeed representatives presenting at All Things Open this year. Join us in Raleigh, NC October 13-15 for engaging discussions.

Flyer for All Things Open shows a cartoon astronaut hitchhiking to Raleigh, North Carolina to get to the conference

Sustaining FOSS Projects by Democratizing the Sponsorship Process

Tuesday, October 15 | 10:45am | Room 201
Speaker: Duane O’Brien, Indeed head of open source

Within a given company, there are typically only a few people involved in deciding which FOSS projects and initiatives to support financially. This year we decided to change all that and democratize the decision making process. We set up an internal FOSS Sustainability Fund and invited everyone to participate in the process.

Sustaining FOSS Projects by Democratizing the Sponsorship Process examines how we got executive buy-in for the fund, set it up, and encouraged participation. It also explores the fund’s impact on our engineering culture.


Using Open Source Tools for Machine Learning

Tuesday, October 15 | 10:45am | Room 301A
Speaker: Samuel Taylor, Indeed data scientist

Machine learning can feel like a magic black box, especially given the wealth of proprietary solutions and vendors. This beginner-friendly talk opens the box. It reveals the math that underlies these services and the open source tools you can use in your own work. It introduces machine learning through the lens of three use cases:

  • Teaching a computer sign language (supervised learning)
  • Predicting energy usage in Texas (time series data)
  • Using machine learning to find your next job (content-based filtering)

You’ll walk away prepared to practice machine learning in the real world.


Your Company Cares about Open Source Sustainability. But Are You Measuring and Encouraging Upstream Contributions?

Tuesday, October 15 | 2:15pm | Room 201
Speaker: Dani Gellis, Indeed software developer

You encourage the behavior that you measure. If you want your company to help sustain the open source projects you depend on, start by measuring how your employees participate in those projects.

How many of your engineers contribute to projects your company consumes? Do they only open issues, or do they contribute code? Are they part of the conversation? Are your non-engineers also involved in the open source community?

Your Company Cares about Open Source Sustainability demonstrates how we use open source tools to measure the velocity of our employees’ open source contributions, as well as how Indeed chose these tools. It covers the evolution of our tooling as our open source program has grown. And it reveals our exciting new initiatives to promote sustainable contributions.

You’ll leave with new ideas for measuring and improving your organization’s contributions to open source projects.


Indeed Open Source: All Things Open 2019—cross-posted on Medium.

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IndeedEng: Proud Supporters of the Open Source Community

At Indeed, open source is at the core of everything we do. Our collaboration with the open source community allows us to develop solutions that help people get jobs.

As active participants in the community, we believe it is important to give back. This is why we are dedicated to making meaningful contributions to the open source ecosystem.

We’re proud to announce our continuing support by renewing our sponsorship for these foundations and organizations.

 

Apache Software Foundation logoThe ASF thanks Indeed for their continued generosity as an Apache Software Foundation Sponsor at the Gold level.

In addition, Indeed has expanded on their support by providing our awesome ASF Infrastructure team the opportunity to leverage Indeed.com job listing and advertising resources. This helped us bring on new hires to ensure Apache Infrastructure services continue to run 24x7x365 at near 100% uptime.

We are grateful for their involvement, which, in turn, benefits the greater Apache community.

— Daniel Ruggeri, VP Fundraising, Apache Software Foundation


 

Cloud Native Computing Foundation logo

CNCF is thrilled to have Indeed as a member of the Foundation. They have been a great addition to our growing end-user community. Indeed’s participation in this vibrant ecosystem helps in driving adoption of cloud native computing across industries. We’re looking forward to working with them to help continue to grow our community.

— Dan Kohn, Executive Director, Cloud Native Computing Foundation


 

Open Source Initiative logoIndeed’s active engagement with open source communities highlights that open source software is now fundamental, not only for businesses, but developers as well.

Like most companies today, Indeed is a user of and contributor to open source software, and interestingly, Indeed’s research of resumes shows developers are too—as job seekers highlight open source skills and experience to win today’s most sought after jobs across technology.

— Patrick Masson, General Manager at the OSI


 

Outreachy logo

We’re so happy that Indeed continues to join our sponsors—making it possible for us to provide critical opportunities to people who are impacted by systemic bias, underrepresentation and discrimination—and helping them get introduced to free and open source software.

— Karen Sandler, Executive Director, Software Freedom Conservancy


 

Python Software Foundation logo

Participation in the PSF Sponsorship Plan shows Indeed’s support of our mission to promote the development of the Python programming language and the growth of its international community.

Sponsorships, like Indeed’s, fund programs that help provide opportunities for underrepresented groups in technology and shows support for open source and the Python community.

— Betsy Waliszewski, Python Software Foundation

 

We’re committed

Our open source initiatives involve partnerships, sponsorships and memberships that support open source projects we rely on. We work to ensure that Indeed’s own open source projects thrive. And we involve all Indeedians. This year we began a FOSS Contributor Fund to support the open source community. Anyone in the company can nominate an open source project to receive funds that we award each month.

We’re committed to open source. Learn more about how we do it.


IndeedEng Supports the Open Source Community—cross-posted on Medium.

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Jobs Filter: Improving the Job Seeker Experience

As Indeed continues to grow, we’re finding more ways to help people get jobs. We’re also offering more ways job seekers can see those jobs. Job seekers can search directly on Indeed.com, receive recommendations, view sponsored jobs or Indeed Targeted Ads, or receive invitations to apply — to name a few. While each option presents jobs in a slightly different way, our goal for each is the same: showing the right jobs to the right job seekers.

If we miss the mark with the jobs we present, you may lose trust in our ability to connect you with your next opportunity. Our mission is to help people get jobs, not waste their time.

Some of the ways we’d consider a job to be wrong for a job seeker are if it:

  • Pays less than their expected salary range
  • Requires special licensure they do not have
  • Is located outside their preferred geographic area
  • Is in a related field but mismatched, such as nurses and doctors being offered the same jobs

To mitigate this issue, we built a jobs filter to remove jobs that are obviously mismatched to the job seeker. Our solution uses a combination of rules and machine learning technologies, and our analysis shows it to be very effective.

System architectureA flowchart of the rule engine library logic used to remove jobs that are obviously mismatched to the job seeker. detailed description below.

The jobs filter product consists of the following components, as shown in the preceding diagram:

  1. Jobs Filter Service. A high throughput, low latency application service that evaluates potential match-ups of jobs to users, identified by ID. If the service determines that the job is appropriate for the user ID, it returns an ALLOW decision; otherwise it returns a VETO. This service is horizontally scalable so it can serve many real-time Indeed applications.
  2. Job Profile. A data storage service that provides high throughput, low latency performance. It retrieves job attributes such as estimated salary, job titles, and job locations at serving time. The job profile uses Indeed NLP libraries and machine learning technologies to extract or aggregate user attributes.
  3. User Profile. Similar to the job profile, but provides attributes about the job seeker rather than the job. Like the job profile, it is a data storage service that provides high throughput, low latency performance. It retrieves job seeker attributes such as expected salary, current job title, and preferred job locations at serving time. Like the job profile, it uses Indeed NLP libraries and machine learning technologies to extract or aggregate user attributes.
  4. Offline Evaluation Platform. Consumes historic data to evaluate rule effectiveness without actually integrating with the upstream applications. It is also heavily used for fine-tuning existing rules, identifying new rules, and validating new models.
  5. Offline Model Training. Component that consists of our offline training algorithms, with which we train models that can be used in the jobs filter rules at serving time for evaluation.

Filter rules to improve job matches

The jobs filter uses a set of rules to improve the quality of jobs displayed to any given job seeker. Rules can be simple: “Do not show a job requiring professional licenses to job seekers who don’t possess such licenses,” or “Do not show jobs to a job seeker if they come with a significant pay cut.” They can also be complex: “Do not show jobs to the job seeker if we are confident the job seeker will not be interested in the job titles,” or “Do not show jobs to the job seeker if our complex predictive models suggest the job seeker will not be interested in them.”

All rules are compiled into a decision engine library. We share this library in our online service and offline evaluation platform.

Although the underlying data for building jobs filter rules might be complex to acquire, most of the heuristic rules themselves are straightforward to design and implement. For example, in one rule we use a user response prediction model to filter out jobs that the job seeker is less likely to be interested in. An Indeed proprietary metric helps us evaluate our performance by measuring the match quality of the job seeker and the given jobs.

Ads ranking and recommender systems commonly rely on user response prediction models, such as click prediction and conversion prediction, to generate a score. They then set a threshold to filter out everything with low scores. This filtering is possible because the models predict positive reactions from users, and low scores indicate poor match quality.

We adopted similar technologies in our jobs filter product, but we used negative matching models when designing our machine learning based rules. We build models to predict negative responses from users. We use Tensorflow to build the Wide and Deep model. This facilitates future experimentation with more complex models such as Factorization machine or neural networks. The features we use cover major user attributes and job data.

After we train a model that performs well, we export it using the Tensorflow SimpleSave API. We load the exported model into our online systems and serve requests using the Tensorflow Java API. Besides traditional classifier metrics such as AUC, precision, and recall, we also load our model into our offline evaluation platforms to validate the performance.

Putting it all to work

We apply our jobs filter in several applications within Indeed. One application is Job2Job, which recommends similar jobs to the job seeker based on the jobs they have clicked or applied for. Using the Job2Job service, we saw a greater than 20% increase in job match quality. When we applied the service to other applications, we observed similar, if not greater, improvements.

Rule-based engines work well in solving corner cases. However, the number of rules can easily spiral out of control. Our design’s hierarchy of rules and machine learning technologies effectively solve this challenge and keep our system working. In the future, we aim to add more features into the model so that it can become even more effective.


Jobs Filter—cross-posted on Medium.

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