Software Engineer, Payment Risk - Global Payment - Global Payment

The Global Payment Tech team is the core engine powering ByteDance's global payment and financial services, spanning payment acquiring, wallets, financial products, insurance, and foreign exchange for users and merchants worldwide.

We span engineering, data, and AI, building high-concurrency, highly available systems across multi-currency, multi-market complexity, and embedding AI into risk control, credit decisioning, and operations automation.

You will grow cross-disciplinary expertise at the intersection of payments, finance, and AI.

The Global Payment team builds platforms and solutions that support payments across ByteDance's products and services, helping businesses deliver secure and reliable payment experiences globally.

The Payment Risk Team provides end-to-end payment risk control solutions, focusing on identifying, assessing, and mitigating risks in global payment transactions. We build capabilities, tools, and platforms for risk analysis, proactive risk detection, and risk intelligence, helping teams discover emerging threats, investigate suspicious activity, and turn insights into risk management decisions.

We are looking for an experienced software engineer to build and evolve tools, LLM-powered applications, and platforms for risk analysis, proactive risk detection, and risk intelligence. You will own projects from problem definition and technical design through production delivery and ongoing improvement, working closely with risk analysts, risk strategy teams, product managers, and fellow engineers.

• Design and build tools for risk signal monitoring, anomaly discovery, transaction and account investigation, and entity relationship analysis; integrate internal risk signals and external intelligence to identify suspicious behavior, uncover emerging patterns, and support timely investigation.
• Develop and productionize LLM-powered applications and AI agents for risk analysis and intelligence, including intelligence collection and extraction, information retrieval, tool-assisted investigation, evidence synthesis, and report generation; establish evaluation and feedback mechanisms to improve output quality, traceability, and usefulness.
• Build and evolve end-to-end platforms and workflows connecting risk signal collection, proactive detection, intelligence enrichment, investigation, and feedback to risk strategies; make risk insights reusable and workflows repeatable, measurable, and easier to scale.
• Own the technical design and delivery of services, data pipelines, and APIs supporting risk analysis, monitoring, and intelligence; improve reliability, performance, observability, and data access controls as usage and business needs grow.
• Work across engineering, product, data, and risk teams to define priorities and deliver solutions; contribute to design and code reviews, share technical knowledge, and drive improvements based on user feedback and production results.

Minimum Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
• Around 5 years of professional software engineering experience, with a track record of independently designing, delivering, and operating production systems or substantial components.
• Strong proficiency in at least one programming language such as Python, Go, or Java, with solid fundamentals in data structures, algorithms, software design, testing, and debugging.
• Experience building backend services, data-intensive applications, or analytical platforms; proficiency in SQL and the ability to work with complex datasets to investigate problems and validate findings.
• Ability to turn ambiguous business requirements into clear technical designs, make sound engineering trade-offs, and own delivery through deployment, monitoring, and iteration.
• Strong analytical and problem-solving skills, with an interest in applying LLMs and AI agents to practical risk analysis, detection, and intelligence problems and evaluating their effectiveness.
• Clear communication, a strong sense of ownership, and experience collaborating across functions in a globally distributed team.

Preferred Qualifications

• Experience in payments, fraud detection, risk management, trust and safety, or related domains, with an understanding of risk monitoring, intelligence analysis, investigation, or strategy workflows.
• Hands-on experience delivering LLM applications or AI agents, including retrieval-augmented generation, tool calling, workflow orchestration, and evaluation of model outputs.
• Experience building risk monitoring systems, intelligence platforms, investigation tools, analytical workbenches, knowledge retrieval systems, or entity and graph analysis capabilities.
• Experience with large-scale data processing, batch or streaming pipelines, and distributed systems supporting analytical workloads.
• Demonstrated impact in improving risk discovery, analyst productivity, intelligence quality, or the adoption of internal tools; experience supporting other engineers through technical reviews and knowledge sharing.