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

ByteDance; San Jose (CA); On-site; USD 254,000 - 480,000; Full time

Benefits offered by this job

Medical/Dental/Vision insurance

401(k) with company match

Parental leave

Disability insurance

Life insurance

Paid holidays

Paid sick days

Paid Personal Time

Job summary

ByteDance Seed in San Jose is seeking PhD‑level researchers to advance large‑scale AI infrastructure, including distributed training, RL frameworks, and multimodal model work.

You will collaborate with researchers and engineers to translate research prototypes into production‑ready systems, optimize GPU and network throughput, and build observability tools.

This role offers bold problems, strong growth, and the chance to shape foundational AI technologies for real products.

Qualifications

• PhD in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Physics, Mathematics, or a related discipline.
• 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).

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.

Skills

Distributed systems

Large-scale ML

RL workflows

Python

C++

PyTorch

Education

PhD in Computer Science, Electrical Engineering, or related field

Tools

Distributed training tools

Job description

Responsibilities

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.

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.

• 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 degree in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Physics, Mathematics, or a related discipline.
• 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 $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:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
• 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.

Why Join ByteDance

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.

As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives.

Our platform connects people from across the globe and so does our workplace.

At ByteDance, our mission is to inspire creativity and enrich life.

To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach.

We are passionate about this and hope you are too.

Reasonable Accommodation

ByteDance is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request