Machine Learning Engineer-TikTok Multimedia, Data Platform-San Jo...
TikTok - San Jose, California, United States, 95199![TikTok](/api/image?src=https%3A%2F%2Fgeneral-board-default.s3.us-east-2.amazonaws.com%2Fpublic%2Fimages%2Fdesk-360x180.webp&w=640&q=75&f=webp)
Work at TikTok
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Overview
TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Singapore, Jakarta, Seoul and Tokyo.
About The TeamThe Multimedia Arch team is responsible for user experience optimization and cost optimization for the whole video on demand and live streaming system. We are building intelligence and personalized end-to-end multimedia systems to power the whole TikTok ecosystem.
We are currently seeking a passionate machine learning engineer to join our team. In this role, you will collaborate with the product and engineering teams to identify opportunities and improve overall system performance and efficiency by integrating AI algorithms across the entire multimedia system. Your responsibilities will include designing service-side ML systems, implementing ML models, and validating their impact on the overall system through A/B testing.
Responsibilities:Develop production machine learning solutions to understand user preferences and video contents, predict video popularity and the change of system resources to build a world class personalized multimedia experience.Collaborate with engineering team and product team to shape the architecture roadmap applying ML.Design and optimize the pipelines for server-side machine learning models, including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.
QualificationsMinimum Qualifications2 years of industry experience developing machine learning models with business impact, and shipping ML solutions to production.A master's or PhD degree in computer science, data science, or a ML related field.Familiar with applied machine learning, such as classification, deep neural networks, transformers, multi-task learning, etc.Proficient in at least one programming language, such as Python and C++.Experience in Deep Learning Tools such as tensorflow/pytorch.Have a passion for learning and an eagerness to experiment with new techniques and good communication and a cooperative spirit.
Preferred QualificationsExperience with recommendation systems, online advertising, search, and causal inference is preferred. Additional familiarity with LLM is a plus.
Job Information:Compensation Description (annually)The base salary range for this position in the selected city is $145000 - $250000 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.Our company benefits are designed to convey company culture and values, to create an efficient and inspiring work environment, and to support our employees to give their best in both work and life. We offer the following benefits to eligible employees:We cover 100% premium coverage for employee medical insurance, approximately 75% premium coverage for dependents and offer a Health Savings Account(HSA) with a company match.Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life and AD&D insurance plans.10 paid holidays per year plus 17 days of Paid Personal Time Off (PPTO) (prorated upon hire and increased by tenure) and 10 paid sick days per year as well as 12 weeks of paid Parental leave and 8 weeks of paid Supplemental Disability.Mental and emotional health benefits through our EAP and Lyra.A 401K company match, gym and cellphone service reimbursements.The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
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