Machine Learning Engineer Graduate (Global E-commerce Recommendation/Search) - 2026 Start (BS/MS)

Location San Jose Employment Type Regular Job Code A163717 Responsibilities The Data‑E-commerce team oversees algorithmic and corresponding engineering work in the e-commerce business. Our responsibilities encompass not only utilizing recommendation and search algorithms to help users discover appealing products but also implementing risk control and content safety algorithms to identify malicious sellers/influencers and violations in products/videos/live streams, ensuring a secure shopping experience. Furthermore, we are involved in constructing intelligent customer service technology and large‑scale product knowledge graphs, aiming to enhance efficiency across various transaction processes. Through operations optimization and intelligent supply chain algorithms, we work to improve logistics efficiency. Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume. Work in a team to conduct cutting‑edge research in machine learning algorithms, such as retrieval and recommendation algorithms. Participate in building large‑scale (10 million to 100 million) e‑commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc. in TikTok. Build long‑term and short‑term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently. Apply machine learning algorithms to improve different business scenarios, such as search ranking, natural language and video understanding, and trust and safety. Qualifications Individuals who are completing or have recently completed a Bachelor's degree or higher in Software Development, Computer Science, Computer Engineering, or a related technical discipline. Excellent analytical and problem‑solving skills. Strong foundation in machine learning and deep learning, with experience in NLP (Natural Language Processing) personalization. Exceptional coding skills with solid knowledge of data structures and algorithms. Proficiency in Linux development environments. Preferred Qualifications Prior experience in search, recommendation, or advertisement algorithms. Familiarity with e‑commerce businesses. Job Information The base salary range for this position in the selected city is $122,574 - $256,000 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 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. Equal Employment Opportunity Statement 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. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues; Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; Exercising sound judgment. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: #J-18808-Ljbffr