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Machine Learning Engineer - E-commerce Merchant and Creator Growth

TikTok

Machine Learning Engineer - E-commerce Merchant and Creator Growth

TikTok

Seattle, WA

·

On-site

·

Full-time

·

1mo ago

Compensation

$129,960 - $246,240

Benefits & Perks

Parental leave program

Learning and development stipend

Top Tier compensation with equity

Health, dental, and vision coverage

Flexible PTO policy

Annual team offsites

Required Skills

Airflow

TensorFlow

PyTorch

About Us

Tik Tok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.

Size: 5001-10000 employees
Industry: Entertainment & Gaming, Social Media, Technology

View Company Profile

Responsibilities

Our Team Supply Side Algorithms:

Our team is committed to expanding the number of merchants and creators on Tik Tok Shop, as well as providing them with comprehensive support to foster growth within the Tik Tok Shop ecosystem. We achieve this by developing end-to-end algorithmic capabilities utilizing machine learning, data mining, and causal inference methodologies.

We are seeking a talented and motivated Machine Learning Engineer with expertise in marketplace growth to join our dynamic and fast-paced team. In this role, you will collaborate with cross-functional teams including data scientists, product managers, and business stakeholders to develop innovative solutions that drive the growth of merchants and creators in Tik Tok Shop.

Responsibilities:

  1. Utilize advanced machine learning techniques to analyze large-scale datasets and identify meaningful, correlations, and causal relations related to merchant and creator growth in Tik Tok Shop
  2. Collaborate with business stakeholders, product managers, and data scientists to define data mining objectives and develop strategies to address complex business problems and opportunities.
  3. Apply feature engineering techniques to derive relevant features and embeddings from raw data and improve the performance of machine learning models.
  4. Develop scalable and efficient data pipelines to preprocess and transform data for machine learning tasks, ensuring data quality, consistency, and availability.
  5. Evaluate and benchmark different machine learning approaches, algorithms, and tools, and recommend the most appropriate solutions based on performance, scalability, and interpretability.
  6. Stay updated with the latest advancements in data mining, machine learning, and related fields, and apply this knowledge to enhance the team's capabilities and identify new opportunities.
  7. Communicate findings, insights, and technical concepts effectively to both technical and non-technical stakeholders, fostering a collaborative and data-driven decision-making culture.

Qualifications

Minimum Qualifications:

  1. Highly self-motivated to drive business growth and foster technical advancement.
  2. Master's or advanced degree in Computer Science, Data Science, Statistics, or a related field.
  3. 3+ years experience as a Machine Learning Engineer, Data Scientist, and experience in causal machine learning
  4. Work experience in user growth, marketing algorithms, recommendation algorithms, advertisement algorithms or related fields
  5. Proficient in using SQL and Python and experience with data manipulation
  6. Experience with big data processing frameworks (e.g., Hadoop, Spark) and distributed computing for efficient data mining on large-scale datasets.

Preferred Qualifications:

  1. Solid understanding of machine/deep learning concepts and techniques, including feature engineering, model evaluation, and optimization.
  2. Strong analytical and problem-solving skills, with a demonstrated ability to handle and derive insights from complex and unstructured datasets.
  3. Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders.

Job InformationFor Pay Transparency Compensation Description (annually)

The base salary range for this position in the selected city is $129960 - $246240 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

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  1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and3. Exercising sound judgment.

Client-provided location(s): Seattle, WA

Job ID: Tik Tok-7356466426959743270

Employment Type: OTHER

Posted: 2024-12-06T16:21:04
Apply on company site

Perks and Benefits

Health and Wellness

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • HSA
  • Life Insurance
  • Fitness Subsidies
  • Short-Term Disability
  • Long-Term Disability
  • On-Site Gym
  • Mental Health Benefits
  • Virtual Fitness Classes

Parental Benefits

  • Fertility Benefits
  • Adoption Assistance Program
  • Family Support Resources

Work Flexibility

  • Flexible Work Hours
  • Hybrid Work Opportunities

Office Life and Perks

  • Casual Dress
  • Snacks
  • Pet-friendly Office
  • Happy Hours
  • Some Meals Provided
  • Company Outings
  • On-Site Cafeteria
  • Holiday Events

Vacation and Time Off

  • Paid Vacation
  • Paid Holidays
  • Personal/Sick Days
  • Leave of Absence

Financial and Retirement

  • 401(K) With Company Matching
  • Performance Bonus
  • Company Equity

Professional Development

  • Promote From Within
  • Access to Online Courses
  • Leadership Training Program
  • Associate or Rotational Training Program
  • Mentor Program

Diversity and Inclusion

  • Diversity, Equity, and Inclusion Program
  • Employee Resource Groups (ERG)

Company Videos

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About TikTok

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

Employees

Los Angeles

Headquarters

$220B

Valuation

Reviews

3.1

3 reviews

Work Life Balance

1.5

Compensation

2.0

Culture

1.2

Career

1.8

Management

1.0

5%

Recommend to a Friend

Pros

Limited positive feedback available

Company size allows for potential opportunities

Technology platform experience

Cons

Mass layoffs and poor handling of terminations

Unprofessional management and HR behavior

Exposure to traumatic content without adequate support

Salary Ranges

52 data points

Mid/L4

Senior/L5

Mid/L4 · Applied AI Product Data Scientist

1 reports

$273,000

total / year

Base

$210,000

Stock

-

Bonus

-

$273,000

$273,000

Interview Experience

4 interviews

Difficulty

3.5

/ 5

Duration

21-35 weeks

Experience

Positive 0%

Neutral 25%

Negative 75%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Interviews

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Data Structures