Software Engineer - Machine Learning & RecommendationsApply for this job
- San Francisco
The target location for this position is the San Francisco Bay Area.
At Pocket we love what we do, but more importantly we are building something that millions of people love, too. Pocket has become essential to how people discover and consume content worthy of their time and attention. Over 30 million people have saved more than 3 billion articles and videos to Pocket to-date.
If you are passionate about using technology to improve people's lives, we believe Pocket has challenges in front of us that will excite you. We're a small team that has the resources of a large company (Pocket was acquired by Mozilla in 2017) and that means lots of opportunity to own things from start to finish that have tremendous impact on large numbers of people, across many different devices and platforms.
With millions of people discovering and consuming content across the Pocket platform, our community generates one of the most unique and interesting sets of data on the web. We are looking to expand our team to help achieve our mission of advancing access to and discovery of high-quality, personalized content across the web. If you are interested in applying your engineering skills to help design, test and launch new data-driven features and products that allow people to focus and become more knowledgeable about the things that interest them, then Pocket might be the place for you!What you’ll do:
- Embody the "voice of the data" - advising our product, engineering and business operations teams and providing input on everything we build, test and analyze.
- Productize, iterate, ship and scale recommendation systems that power user-facing features used by millions of people every day.
- Conceptualize, design and prototype new uses of our data set to enrich the Pocket user experience.
- Contribute to a world-class team and shape the future of data at Pocket.
- You love combining data + engineering and problem solving how to create incredible experiences at their intersection.
- You have an academic background in computer science or equivalent work experience.
- You have at least 2 years professional experience and direct experience and/or passion around data science topics like NLP, IR, Search and/or ML. Consumer-facing product experience a plus.
- You are a strong programmer with experience with PHP, Python, Ruby, Go, Java or equivalent (and comfortable writing code without support of frameworks).
- You have worked with large, complex data sets and used statistics, data mining and modeling to extract useful data and insights.
- You possess strong communication skills and an ability to approach problems in a structured way and distill complex issues into actionable insights.
- You stay current with the latest/most relevant data science technologies and trends.
- While not required, any direct experience with text mining and parsing, neural networks, classification, designing and implementing data pipelines, Elasticsearch or equivalent is all considered a plus.
Pocket (Read It Later, Inc) was founded in 2007 by Nate Weiner to help people save interesting articles, videos and more from the web for later enjoyment. Once saved to Pocket, the list of content is visible on any device — phone, tablet or computer. It can be viewed while waiting in line, on the couch, during commutes or travel — even offline.
The world's leading save-for-later service currently has more than 30 million registered users and is integrated into more than 1,500 apps. It is available for major devices and platforms including iPad, iPhone, Android, Mac, Kindle Fire, Kobo, Google Chrome, Safari, Firefox, Opera and Windows
Mozilla is committed to Equal Employment Opportunity throughout our recruiting and hiring process and is dedicated to growing diversity in our workplace.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.