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Pinterest
Pinterest

Discover and do what you love.

Machine Learning Engineer II

직무머신러닝
경력미들급
위치Dublin, Ireland, United States
근무오피스 출근
고용정규직
게시1개월 전
지원하기

필수 스킬

Python

SQL

PyTorch

Spark

Machine Learning

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

We’re looking for a machine learning engineer II in our Growth Platform engineering group. You’ll join a small and focussed team who will help us to understand our users better so we can drive engagement and growth, and to improve their user experience.

What you'll do:

  • Develop and implement ML models to improve user targeting and personalization for growth initiatives

  • Design and build scalable ML pipelines for data processing, model training, and deployment

  • Collaborate with cross-functional teams to identify potential ML solutions for growth opportunities

  • Conduct A/B tests to evaluate the performance of ML models and optimize their impact on key growth metrics

  • Analyze large datasets to extract insights and inform decision-making for user acquisition and retention strategies

  • Contribute to the development of our ML infrastructure, ensuring it can support rapid experimentation and deployment

  • Stay up-to-date with the latest advancements in ML and recommend new techniques to enhance our growth efforts

  • Participate in code reviews and collaborate with team members as needed

What we're looking for:

  • 3+ years of experience applying ML to real-world problems, preferably in a growth or user acquisition context

  • Excellent communication skills and ability to work effectively in cross-functional teams

  • Strong problem-solving skills and ability to translate business requirements into technical solutions

  • Strong programming skills in Python and experience with Py Torch

  • Proficiency in data processing and analysis using tools like SQL, Spark, or Hadoop

  • Experience with recommendation systems, user modeling, or personalization algorithms

  • Familiarity with statistical analysis

  • Bachelor’s/Master’s degree in a relevant field or equivalent experience.

Nice to have:

  • Experience with Natural Language Processing (NLP) to enhance user targeting and personalization strategies

  • Proficiency in data visualization techniques

  • Experience with cloud platforms (e.g., AWS) and containerization technologies (e.g., Docker, Kubernetes)

  • Contributions to open-source ML projects or research publications

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our Pin Flex page to learn more about our working model.

In-Office Requirement Statement:

  • We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.

  • This role will need to be in the office for in-person collaboration 1-2 times per month and therefore needs to be in a commutable distance from one of the following offices: Dublin.

Our Commitment to Inclusion:

Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.

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Pinterest 소개

Pinterest

Pinterest

Public

Pinterest is an American social media service for publishing and discovery of information in the form of digital pinboards. This includes recipes, home, style, motivation, and inspiration on the Internet using image sharing. Pinterest, Inc.

1,001-5,000

직원 수

San Francisco

본사 위치

$18.7B

기업 가치

리뷰

10개 리뷰

3.8

10개 리뷰

워라밸

3.8

보상

2.7

문화

4.1

커리어

2.5

경영진

3.0

65%

지인 추천률

장점

Flexible work arrangements and remote options

Great team culture and collaboration

Work-life balance

단점

Limited career advancement and growth opportunities

Below average salary compensation

Management responsiveness and direction issues

연봉 정보

63개 데이터

Junior/L3

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Machine Learning Engineer I

8개 리포트

$153,318

총 연봉

기본급

$148,699

주식

-

보너스

-

$153,318

$153,318

면접 후기

후기 3개

난이도

3.0

/ 5

소요 기간

14-28주

면접 과정

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

자주 나오는 질문

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit