Research Engineer - Reinforcement Learning (RL) Systems & Infrastructure (Seed Infra)

ByteDance

$254K — $480K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Strong background in distributed systems or ML infrastructure.
  • Experience in building or optimizing large-scale training systems such as RL or LLM.
  • Proficient engineering skills in Python/C++ with modern ML frameworks.
  • Experience optimizing GPU performance and system-level tuning.
  • Understanding of reinforcement learning workflows.

Responsibilities

  • Design and build end-to-end RL systems for large-scale models.
  • Develop fault-tolerant RL infrastructure for dynamic workloads.
  • Optimize distributed training performance across GPU clusters.
  • Collaborate with researchers on system-algorithm co-design.
  • Build monitoring and debugging frameworks for RL training systems.

Benefits

  • Day one access to medical, dental, and vision insurance.
  • 401(k) savings plan with company match.
  • Paid parental leave and disability coverage.
  • Life insurance and wellbeing benefits.
  • 10 paid holidays and 10 sick days per year.
  • 17 days of Paid Personal Time, increasing by tenure.
Full Job Description
Responsibilitie

About the Team The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. Responsibilities - Design and build end-to-end reinforcement learning (RL) systems for large-scale models, covering rollout, training, evaluation, and deployment pipelines. - Develop scalable and fault-tolerant RL infrastructure that operates efficiently under dynamic workloads and heterogeneous compute environments. - Optimize distributed training performance across GPU clusters, improving throughput, resource utilization, and system stability. - Collaborate with cross-team researchers on targeted system-algorithm co-design to translate research ideas into robust, production-grade implementations. - Build tooling, monitoring, and debugging frameworks to ensure reliability and observability of large-scale RL training systems.

Qualification

Minimum Qualifications: - Strong background in distributed systems, large-scale ML systems, or deep learning infrastructure - Experience building or optimizing large-scale training systems (e.g., RL, LLM, multimodal models) - Solid engineering skills in Python/C++ and familiarity with modern ML stacks (PyTorch, distributed training frameworks, etc.) - Experience with GPU optimization, parallelism strategies, and system-level performance tuning - Understanding of reinforcement learning workflows (rollout, policy update, evaluation loops) Preferred Qualifications: - Experience with large-scale agent systems - Familiarity with system design under heterogeneous or dynamic workloads - Exposure to RL + LLM training or post-training pipeline

Job Information

【For Pay Transparency】Compensation Description (Annually)

The base salary range for this position in the selected city is $254400 - $480000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

About Doubao (Seed)

Established in 2023, the ByteDance Seed team is dedicated to pioneering new paths toward artificial general intelligence. We aspire to advance the frontier of intelligence to drive progress for both technology and society.

With a long-term vision for the AI sector, the Seed team's research spans MLLM, GenMedia, AI for Science, and Robotics. We maintain a global presence with laboratories and career opportunities across China, Singapore, and the United States. To date, we have launched industry-leading general foundation models and cutting-edge multimodal capabilities. Our technology powers over 50 application scenarios - including Doubao, Jimeng, TRAE, Dola and Dreamnia - and serves enterprise customers through Volcano Engine and BytePlus. Third-party data shows that the Doubao App ranks first in user volume in the Chinese market, while Doubao foundation models lead the industry in average daily token consumption.

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