AI Risk Governance & Quality Gates: Establish Agent behavior boundaries, hallucination interception, and Fallback mechanisms; build automated quality gates for AI-generated code, ensuring every stage's output is verified before it flows downstream.
Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life - a mission we work towards every day.
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
Mentor junior AI engineers, set best practices for prompt engineering, RAG pipelines, and agent evaluation, and help build reusable AI infrastructure for the broader team.
Support the development of AI-enabled products from concept through delivery, with a focus on quality, usability, scalability, and responsible implementation
Select the appropriate architecture for each use case rather than applying multi-agent patterns by default:Deterministic workflow and state-machine architectures for repeatable, high-confidence operational processes.Planner-executor architectures for decomposing complex investigation, planning, and remediation tasks.Supervisor-worker or manager-worker architectures for coordinating specialist domain agents.Router architectures for selecting the right model, tool, knowledge source, workflow, or specialist agent.Reflection, critic, verifier, or judge patterns for quality assurance, validation, and safety checks.Event-driven architectures for responding to telemetry anomalies, workload failures, scheduler events, benchmark regressions, and operational alerts.Human-in-the-loop architectures for high-impact recommendations, privileged actions, or changes to production environments.
Platform Development: Develop and manage the necessary tools and algorithms platforms, assisting in comprehensively improving the accuracy, performance, and data generalization of deployed models.
Model deployment and integration: Collaborate with software engineers and DevOps teams to deploy machine learning models into production environments. Implement APIs and integrate models with existing systems and applications to enable real-time decision-making.
Develop labeling systems that support dataset segmentation, coverage analysis, quality filtering, training-data mixtures, and model performance attribution.
Embed Responsible AI: Build evaluation frameworks, guardrails and monitoring capabilities to manage AI reliability, hallucinations, security and data privacy, incorporating human-in-the-loop review, auditability and appropriate escalation pathways.
You will drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks
Integrate the digital stack. Connect models and AI services with enterprise platforms, data sources, workflow systems, user interfaces and upstream or downstream applications.