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# Staff Machine Learning Engineer

- **Company:** COMMUNITY HEALTH PARTNERS
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $254,400.0
- **Contract:** Internship / Graduate position
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Big Data, C++ (Programming Language), Email Filtering, Apache Hadoop, Apache Hive, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Large Language Models, Apache Spark, Deep Learning, Generative AI, Information Technology, Data Analytics, Live Streaming, Machine Learning Operations
- **Published:** August 31, 2026
- **Apply:** https://www.dice.com/job-detail/58f97e63-2689-43f0-9fee-07a5ff5a88bd

## About the Role

Minimum Qualifications: - Master's degree or above in Computer Science, Statistics, Machine Learning, or another relevant technical field, with at least 2 years of hands-on machine learning experience through industry, research, internships, or equivalent project work. - Strong software engineering fundamentals and proficiency in Python or one of Java/C++/Go, with experience in large-scale data processing technologies such as Spark, Hadoop, or Hive. - Strong machine learning fundamentals, with research or hands-on experience in areas such as deep learning, representation learning, graph learning, sequence/time-series modeling, transfer/multi-task learning, or unsupervised/self-supervised learning. - Strong problem-solving and analytical skills, with the ability to reason and communicate in a result-oriented and data-driven manner. - Natural curiosity and a strong passion for solving complex, ambiguous problems; willingness to dig deep, challenge assumptions, and continuously
explore better solutions. - Strong collaboration and communication skills, with the ability to work effectively across engineering, product, data, system and other cross-functional teams. - Ability to work with a high degree of autonomy, learn quickly, and adapt to a rapidly evolving risk environment. Preferred Qualifications: - Industry experience in risk, fraud, spam, abuse detection, or related areas is preferred but not required. - Experience building or deploying large-scale machine learning systems/algorithms is a plus. - Hands-on experience with LLMs, generative AI, or agent development, including LLM-powered applications, evaluation pipelines, retrieval or knowledge systems, or agentic workflows. - Research publications or strong research experience in relevant machine learning areas are a plus.

## Description

The Business Risk Integrated Control (BRIC) team is missioned to: - Protect TikTok users, including and beyond content consumers, creators, advertisers and other participants across the ecosystem; - Safeguard platform health and community experience authenticity; - Build scalable infrastructure, platforms, and technologies while collaborating closely with cross-functional teams and stakeholders. The BRIC team works to minimize the impact of inauthentic and abusive behaviors across TikTok products and platforms. Our scope covers a broad range of community and business risk areas, including account integrity, engagement authenticity, anti-spam, API abuse, growth fraud, live streaming security, and financial safety across advertising and e-commerce. In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems
and solutions. Our challenges are not some regular day-to-day technical puzzles - You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to make quick and solid differences. Responsibilities: - Build machine learning solutions to respond to and mitigate business risks in TikTok products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc. - Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups. - Advance machine learning capabilities in areas such as risk perception and analysis, model interpretability, privacy and compliance, and adversarial robustness.


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