Job Description

The AI Data and Safety team plays a critical role in advancing Seed's foundational models, AI products across modalities, and improving AI-native applications built on the Seed model series. We work across the data lifecycle, from defining evaluation approaches, translating user feedback and benchmark outcomes into data requests, to building scalable processes that improve data quality and support rapid model iteration.

Our team combines technical and operational capabilities, bringing together multidisciplinary and multilingual talent across product management, data engineering, and data operations. Our work is driven by people who think deeply about model behavior, move quickly to solve complex problems, and bring first-hand experience as both builders and users of models and agents.

In close partnership with internal researchers, industry experts, and leading data vendors, we tackle challenging data problems at the frontier of AI development, helping improve both model performance and user experience.

As more large AI models are being developed, high-quality data has become the core fuel driving the leap in model capabilities. Our team – AI Data & Safety – Data Annotation and Evaluation Operations – is the builder and operator of this critical link.

Our value lies in transforming the "intelligence" scattered across individual experience and organizational knowledge into "data" that models can understand and learn from by establishing a scientific and efficient data production and operation system. This directly drives the iteration of model capabilities and the implementation of various AI applications. We serve not only as key infrastructure supporting ByteDance’s AI strategy, but also as the core bridge connecting human wisdom and machine intelligence.

Our specific responsibilities include providing data training, model evaluation, model operation, and user growth for ByteDance’s large model business, driving continuous improvement and application of model capabilities.

As an Analyst, you will have the opportunity to engage in impactful projects that provide you with plenty of professional, real-world experience in the AI industry. You will gain practical skills through on-the-job learning in a fast-paced work environment and have opportunities to solidify your foothold in the fast-growing AI space.

Applications will be reviewed on a rolling basis - we encourage you to apply early.

Your Role Will Involve:
1. Project Management: Lead and manage data annotation, evaluation, and/or user growth projects for various AI product modalities (LLM, VLM, Speech or Video) across multiple General, STEM, or non-STEM academic topics. Ensure that timelines, quality standards, and objectives are met appropriately with meticulous planning. Track project progress, identify risks, and implement corrective actions as necessary to keep projects on course. Build and maintain strong relationships with product managers, researchers, data annotators, and other cross-functional team members. Communicate project updates, address concerns, and align expectations to ensure successful project outcomes. Engage with external vendors and experts per project demands and scale project productivity.
2. Workflow Design and Management: Design, manage, and optimize workflows for each project you own, including training design, data annotation or QA processes, and performance tracking to meet project needs. Proactively plan and perform quality and productivity improvements to enhance operational processes. Develop and maintain technical guidelines and casebooks to support consistent, high-quality data production. Collaborate closely with product managers, project leaders, cross-functional teams and external collaborators to ensure alignment on quality metrics and project expectations.
3. Data Checking and Analysis: Design and implement robust data analysis strategies to evaluate training and evaluation datasets. Ensure the mathematical accuracy and statistical validity of all project data. This includes designing and implementing robust data checking protocols, performing deep-dive analysis to identify trends and anomalies, and translating quantitative findings into actionable insights for model improvement in reports. You will collaborate with data annotators, researchers, and product managers to define quality benchmarks and ensure data-driven decision-making throughout the project lifecycle.
4. Continuous Learning: Regularly follow the progress of competitor large models and related cutting-edge technologies, continuously explore efficient data production methods such as automated data scraping, model evaluation, and Agentic/Code-based data synthesis, and become an expert in one or more content verticals (such as Pre-training, Math or MultiModal Machine Learning). Foster a collaborative environment, sharing new learnings and best practices for knowledge transfer within the team.