ByteDance, Seattle, Washington
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.
Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.
Strong programming skills in Python and proficiency with ML frameworks (e.g., PyTorch, TensorFlow, JAX).
Hands-on experience building or evaluating LLM-based agent systems (e.g., ReAct, function calling, code generation agents, or multi-agent orchestration). Familiarity with meta-learning, few-shot generalization, or transfer learning in the context of LLM-based systems.
Strong interest in bridging frontier AI research with production-grade engineering — turning papers into systems that work at scale.
The Applied Machine Learning Ark team combines system engineering and machine learning to develop and operate Large Language Model (LLM) service platforms that offer businesses Model-as-a-Service (MaaS) solutions, serving both large model providers and downstream users. The US team drives the design, development, and operation of MaaS solutions across the US and international markets outside mainland China. We are building full-stack, end-to-end solutions spanning text and multimodal LLM algorithms, LLM training/fine-tuning/inference frameworks, prompt engineering, model alignment, and intelligent agent systems.
Beyond model serving, we operate large-scale log analytics pipelines that process massive volumes of invocation logs from text models, multimodal models, and agent systems — extracting usage patterns, quality signals, and actionable insights to inform model improvement, system optimization, and product decisions through continuous, data-driven feedback loops. We are actively seeking talented engineers and researchers specializing in Large Language Models and AI Agent systems to join our dynamic team.
As model capabilities improve and computation becomes cheaper, the key challenge in real-world deployment is no longer building a capable one-off assistant, but building agent systems that improve through use. This research studies a self-evolving agent framework in which execution traces, environmental responses, and human feedback are converted into signals for continual improvement. The goal is to establish a closed loop from execution to feedback, attribution, accumulation, and reuse, so that system capability grows with real-world interaction.
We focus on three tightly coupled directions: adaptive runtime, which enables online adjustment of planning, tool use, and control policies; experience compilation, which abstracts reusable skills, rules, and failure patterns from trajectories; and evaluation-governance loops, which ensure that each system update is measurable, comparable, and reversible. Together, these components support a synergistic co-evolution of the model layer and the harness layer, improving task quality, reducing manual intervention, and accumulating durable capability over time.
More broadly, this work reframes agent deployment as a continual learning systems problem: not how to build a stronger static agent, but how to build an operational system that learns reliably from experience.
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