Research Engineer Graduate (AI Training Systems & RL Infrastructure - Seed Infra) - 2026 Start (PhD)

About the role

The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. We are looking for talented individuals to join our team in 2026.

Responsibilities

• Conduct research and development on large-scale AI infrastructure to support efficient training and post-training of foundation models, multimodal LLMs, and image/video generation models.
• Design and optimize distributed training strategies, including data/model/tensor/pipeline/expert parallelism, computation–communication overlap, and large-scale GPU cluster scaling.
• Prototype and improve end-to-end reinforcement learning (RL) training systems, covering rollout generation, policy optimization, evaluation, and iterative deployment workflows.
• Build scalable and fault-tolerant infrastructure that operates reliably under dynamic workloads and heterogeneous compute environments.
• Analyze performance bottlenecks across the training stack (e.g., networking, scheduling, GPU memory management), and develop principled optimization approaches to improve throughput, efficiency, and stability.
• Develop tooling, monitoring, debugging, and observability frameworks to ensure reliability of large-scale training and RL systems.
• Collaborate with researchers and engineers on system–algorithm co-design, translating research prototypes into scalable, production-ready infrastructure systems.

Qualifications

• Minimum Qualifications: Individuals who are completing or have recently completed a PhD in Computer Science, Electrical Engineering, or a related technical field (graduating students welcome).
• Strong background in distributed systems, large-scale machine learning systems, or deep learning infrastructure.
• Research or hands-on experience in training or optimizing large-scale models (e.g., LLMs, multimodal models, RL systems).
• Understanding of parallelism strategies (e.g., data, model/tensor, pipeline, expert parallelism) and distributed training concepts.
• Familiarity with reinforcement learning workflows such as rollout generation, policy optimization, and evaluation loops.
• Proficiency in programming (e.g., Python and/or C++) and experience with modern ML frameworks (e.g., PyTorch and distributed training tools).

Job Information

[For Pay Transparency] Compensation Description (Annually) The base salary range for this position in the selected city is $244800 - $450000 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).