This is a start up-style role with broad ownership across AI development, data collection, robot integration, testing, failure analysis, and continuous performance improvement. The ideal candidate is comfortable working across disciplines and taking ideas from early experimentation through to real-robot deployment.
We are looking for a hands-on Embodied AI Engineer with experience in robot learning to develop, fine-tune, deploy, and validate intelligent behaviors on physical humanoid robots.
Work with Product, Frontend, Payments and Operations to test an order from payment confirmation to first data use. Check that the package a customer receives matches the offer on the website and in the app.
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.
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.
Master's degree or above, with at least 3 years of hands-on experience in Reinforcement Learning (RL), Reinforcement Learning from Human Feedback (RLHF), or Reinforcement Learning with Verifiable Rewards (RLVR). Experience in Agent-related projects is preferred.
Master's degree or above, with at least 3 years of hands-on experience in Reinforcement Learning (RL), Reinforcement Learning from Human Feedback (RLHF), or Reinforcement Learning with Verifiable Rewards (RLVR). Experience in Agent-related projects is preferred.
