| Company: | Netflix |
|---|---|
| Job Role: | Sr. Software Engineer Data & Feature infrastructure, ML Platform |
| Experience: | Minimum (4 years): |
| Vacancy: | 50 |
| Qualification: | BE/B Tech /Bsc/BCA MTech/MCA etc.. |
| Salary: | Based On Performance |
| Location: | Los Gatos, California |
| Join us on Telegram | Click Here |
| Apply Mode: | (Online) |
| Deadline: | Not Mentioned |
A fantasy team1 is one in which the entirety of your partners are phenomenal at what they do and are profoundly viable teammates. The worth and fulfillment of being in a fantasy group is enormous. Our adaptation of the extraordinary working environment isn't incredible rec centers, extravagant workplaces, or regular gatherings. Our adaptation of the extraordinary work environment is a fantasy group in quest for driven shared objectives, for which we spend vigorously. It is in such a group that you gain proficiency with the most, play out your best work, improve the quickest, and have a great time.
To have a whole organization contain the fantasy group (as opposed to only a couple little gatherings) is testing. Undeniably, we need to enlist well. We likewise need to encourage joint effort, embrace a variety of perspectives, support data sharing, and debilitate governmental issues. The uncommon part is that we give satisfactory entertainers a generous2 severance bundle so we can discover a star for that position. On the off chance that you think about a pro athletics group, it is dependent upon the mentor to guarantee that each player on the field is stunning at their position, and plays viably with the others. We model ourselves in being a group, not a family. A family is about unrestricted love, in spite of, say, your kin's terrible conduct. A fantasy group is tied in with driving yourself to be the best colleague you can be, thinking often seriously about your partners, and realizing that you may not be in the group for eternity.
ML models must be pretty much as great as the information that we give to them, which is the reason we keep on developing on making highlight designing as straightforward, adaptable and solid as could really be expected. Might you want to construct and scale our Data and Feature Infrastructure that forces different ML and advancement use cases spreading over the whole lifecycle of a substance from pitch to play? This envelops figuring out what pitches to green light, deciding the delivery date, prioritization of advertising spending plans, and doing customized proposals for our individuals to find and appreciate watching it.
The Opportunity
In this job, you will have the chance to fabricate adaptable reality and highlight stores that ML experts can undoubtedly use to collect excellent preparing datasets for assorted ML use cases across Netflix. Opening admittance to these datasets will encourage advancement through ML in new business regions that in any case wouldn't have been possible. You will work with the remainder of the ML Platform association while upgrading Metaflow to give a firm end client experience that extraordinarily improves the usefulness of ML experts. In this job you will acquire private information on Netflix personalization models, content interest and valuation models and so on while working for an extraordinary and spearheading organization that is rethinking how video content is devoured all around the world.
Here are a few instances of the sorts of things you would deal with:
- Plan and oversee actuality stores that can be utilized for creating ML highlights for a subjective time frame in the past with no on the web/disconnected skewness
- Normalize the age of ML highlights, assemble and scale include stores that can be utilized to effectively find and offer highlights and productively load them into preparing structures like PyTorch and TensorFlow
- Increment ML specialist efficiency by making it simple to get to and investigate information for disconnected experimentation and productization
- Fabricate and upgrade libraries that can effectively peruse huge datasets from S3
Least Qualifications
- 4+ long periods of significant experience building ML framework
- Solid compassion and enthusiasm for giving a fabulous client experience to ML professionals
- Involvement with huge scope information handling systems and columnar information structures
- Experience working and enhancing Python based information pipelines
- Involvement in Cloud Computing stages like Amazon AWS
Favored Qualifications
- Experience working with Spark and Scala
- Experience working with compartment (Docker) stages
- Experience working with Notebooks like Jupyter or Polynote.
- Netflix is an equivalent chance boss and endeavors to assemble assorted groups from varying backgrounds. We offer a novel culture of opportunity and obligation with a reasonable long haul perspective on our business. We prescribe perusing these to comprehend what working at Netflix resembles.
Required Employee Information ;
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