LLM Backend Engineer Graduate - Applied Machine Learning

Volcano Ark is an all-in-one large model service platform launched by Volcano Engine. It is a leading platform in China's large model market by product capability and market share. The platform provides end-to-end services including model inference, evaluation, fine-tuning, AI application development, and a plugin ecosystem. Volcano Ark hosts Doubao and leading industry large models, and supports enterprise AI adoption through secure and trusted solutions as well as professional algorithm and technical services. Data AML is ByteDance's machine learning platform team. It provides training and inference systems for recommendation, advertising, computer vision, speech, and NLP scenarios across products such as Douyin, Toutiao, and Xigua Video. The team also supports internal business teams with large-scale machine learning compute, explores general and innovative algorithms for business problems, and offers core machine learning and recommendation system capabilities to external enterprise customers through Volcano Engine. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.​ Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.​ Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities: - Design and develop core components of the Volcano Ark MaaS platform, supporting API capabilities for text dialogue, multimodal understanding, and multimodal generation. - Participate in cloud-native architecture evolution, including Service Mesh, load balancing, and intelligent routing; design and implement high-availability solutions such as end-to-end grayscale release, traffic degradation, circuit breaking, and rate limiting. - Perform system-level performance tuning for long connections, high throughput, streaming output, and low latency in multimodal large model inference scenarios. - Ensure system stability for large-scale model invocation scenarios and resolve architectural bottlenecks caused by sudden traffic spikes. Minimum Qualifications: - Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science or a related discipline. - Proficient in at least one programming language such as Golang, Java, C++, or Python; has practical software development experience and solid computer science fundamentals. - Understands basic backend engineering systems, including databases, computer networks, operating systems, and distributed system principles. Preferred Qualifications: - Hands-on experience in server-side engineering. - Understanding of Agent concepts and related technical implementations, such as Function Calling and MCP. - Familiarity with cloud-native and service mesh technologies such as Kubernetes, Docker, Istio, and Envoy. - Interest in large model inference pipelines, with relevant background knowledge.