Applied Al, Applied Scientist - Trust & Safety - San Jose

Key Responsibilities:
• Collaborate with cross-functional stakeholders to gather and refine requirements for data labeling projects and identify opportunities for optimization through data-driven solutions.
• Design and manage the full lifecycle of end-to-end data labeling and policy testing workflows - from aligning with business needs to deployment, iteration, and monitoring.
• Establish and maintain a centralized knowledge base for Retrieval-Augmented Generation (RAG) systems, incorporating both structured (e.g., SOPs, guidelines) and unstructured (e.g., annotations, case logs) data to support LLM-based policy QA and labeling efforts.
• Operationalize intelligent labeling pipelines leveraging Prompt Engineering, agent-based workflows, and labeling models to ensure availability of high-quality data for model training and policy evolution.
• Translate complex policy documents into machine- and human-readable formats, support agent and PE strategy development, and evolve nuanced policy edge cases in sync with fast-changing regulatory or platform dynamics.
• Apply multi-modal LLM techniques to extract latent signals from content that inform moderation strategies and highlight policy gaps.
• Lead applied ML and data science research and experimentation to solve business-critical use cases.
• Own the model lifecycle from data sourcing and preprocessing to training, deployment, and post-launch maintenance.

Qualifications

Minimum Qualifications:
• Advanced degree (Master's or Ph.D.) in Statistics, Computer Science, Applied Mathematics, Data Science, or a related quantitative field.
• Strong theoretical foundation in computer science, machine learning, and statistics, with industry experience in deep learning and at least one of the following: Prompt Engineering, LLMs, CV, NLP, or speech recognition.
• In-depth experience in unsupervised learning, clustering algorithms, and pattern recognition from unstructured data such as text or video.
• Experience in data project management, and solid foundations of maths and algorithms
• Expertise in SQL, Hive, Presto, or Spark, and experience with large-scale datasets;
• Proficiency in Python and Deep Learning frameworks such as TensorFlow or PyTorch

Preferred Qualifications:
• At least 3 years of experience in software development or model/data pipeline development, with hands-on experience applying LLM technologies (e.g., Test Time Scaling, Chain of Thought, Retrieval-Augmented Generation, Supervised Fine-Tuning) to real-world problems.
• Deep understanding of data pipeline architecture, model development lifecycle, testing, and deployment.
• Practical industry experience in applying prompt engineering and emerging Al techniques to address diverse business needs.
• Demonstrated strong intellectual curiosity, excellent problem-solving skills, and advanced analytical abilities to deconstruct problems, identify root causes, and propose effective solutions.
• Proven track record of success in high-growth, fast-paced, and ambiguous environments.
• Excellent communication and collaboration skills, with the ability to work effectively across global teams and stakeholders.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $114000 - $240000 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:

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; and

3. Exercising sound judgment.