Software Engineer (Leadership) - Machine Learning - Facebook (California)

Intro:

Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.

Summary:

Facebook is seeking Machine Learning Engineers to join our engineering team. The ideal candidate will have industry experience working on a range of classification and optimization problems like payment fraud, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection. The position will involve taking these skills and applying them to some of the most exciting and massive social data and prediction problems that exist on the web.

Required Skills:

1. Drive the team's goals & technical direction to pursue opportunities that make your larger organization more efficient

2. Effectively communicate complex features & systems in detail

3. Understand industry & company-wide trends to help assess & develop new technologies

4. Partner & collaborate with organization leaders to help improve the level of performance of the team & organization

5. Identify new opportunities for the larger organization & influence the appropriate people for staffing/prioritizing these new ideas

6. Suggest, collect and synthesize requirements and create an effective feature roadmap

7. Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules-based models

8. Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)

Minimum Qualifications:

9. Experience leading projects with industry-wide impact

10. Experience communicating and working across functions to drive solutions

11. Experience in mentoring/influencing senior engineers across organizations

12. Proven track record of planning multi-year roadmap in which shorter-term projects ladder to the long-term vision

13. Experience in driving large cross-functional/industry-wide engineering efforts

14. 12+ years of experience in programming languages (Python, C++, Java or R) with technical background

15. 8+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, NLP, data mining or artificial intelligence

Preferred Qualifications:

16. B.S. Computer Science or related technical field

17. Experience in shipping products to millions of customers or have started a new line of product

18. Experience with Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Hive/Spark

**Industry:** Internet

**Equal Opportunity:** Facebook is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Facebook is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@fb.com.



* This article was originally published here

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