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职位Netflix

Machine Learning Engineer (L5) - Content Understanding

Netflix

Machine Learning Engineer (L5) - Content Understanding

Netflix

USA - Remote

·

Remote

·

Full-time

·

10mo ago

薪酬

$419,400 - $675,000

福利待遇

Healthcare

Mental Health

401(k)

Equity

Parental Leave

必备技能

Machine Learning

Deep Learning

Supervised Learning

Unsupervised Learning

Gen AI

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

The goal of our Content Understanding team is to enable operational and creative excellence in the distribution and promotion of our content on our service. We collaborate closely with our partners in the Product Discovery & Promotion organization, and our work directly contributes to launching high-quality content on our service and helps our members discover content they will love. We conduct analyses, build analytical tools, and develop models to help our partners execute on these primary objectives.

We are looking for a talented machine learning engineer to join our Merchandising & Content Understanding pod, which focuses on deepening our content metadata across all formats and improving the discovery experience on our service. You will design and develop models and infrastructure for algorithms that will power the next generation of capabilities for our business. You will partner with our world-class team of creative production practitioners and various cross-functional teams to shape strategy and deliver impact via machine learning and artificial intelligence solutions. Interested? Read more about the job description and qualifications below!

What you will do:

  • Collaborate closely with stakeholders in Product Discovery & Promotion to learn deeply about content metadata and merchandising and identify potentially impactful problems to solve via scalable machine learning and artificial intelligence solutions

  • Develop innovative systems and models that empower decision-making for stakeholders and product features that can deliver member joy by leveraging a wide variety of metadata and production media generated by and collected from our productions throughout their end-to-end lifecycle

  • Collaborate with team members and cross-functional partners to operationalize your models so that they can be integrated seamlessly into operational workflows

  • Serve as a key thought partner for stakeholders, cross-functional partners, and our diverse set of team members regarding machine learning algorithms and system architectures

Your background and characteristics:

  • Ph.D. or MS degree in a quantitative or computational field

  • 4+ years of full-time work experience in one or more relevant machine-learning roles

  • Practical experience in supervised, unsupervised, and deep machine learning methods

  • Practical experience applying machine learning and Gen AI solutions to video, audio, and/or textual data sources

  • Practical experience operationalizing or productionizing machine learning and/or artificial intelligence solutions

  • Comfortable and effective in ambiguous problem spaces; ability to own and drive projects with minimal oversight and process

  • Exceptional written and oral communication with technical and non-technical audiences

Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $419,400.00 - $675,000.00. This compensation range will vary based on location.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.

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关于Netflix

Netflix

Netflix

Public

An online streaming platform that enables users to watch TV shows and movies.

10,001+

员工数

Los Gatos

总部位置

$280B

企业估值

评价

3.8

10条评价

工作生活平衡

2.5

薪酬

4.2

企业文化

3.8

职业发展

4.0

管理层

3.2

68%

推荐给朋友

优点

Great benefits and perks

Supportive team and culture

Competitive salary and compensation

缺点

Fast-paced and high pressure environment

Work-life balance issues

High workload and long hours

薪资范围

1,875个数据点

L3

L4

L5

L6

Mid/L4

Senior/L5

L3 · Data Scientist

0份报告

$242,500

年薪总额

基本工资

-

股票

-

奖金

-

$206,125

$278,875

面试经验

4次面试

难度

4.0

/ 5

录用率

25%

体验

正面 25%

中性 25%

负面 50%

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

System Design Interview

5

Behavioral Interview

6

Team Matching

7

Final Round

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit