Research Scientist in AI Foundation Model Infrastructure - Seed - Graduates - 2027 Start (BS/MS[...]

ByteDance; Seattle (WA); On-site; USD 202,160 - 368,220; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) savings plan with company match

10 paid holidays per year

17 days of Paid Personal Time

Paid parental leave

Short-term and long-term disability coverage

Wellbeing benefits

Job summary

A leading technology firm in Seattle is seeking a Research Scientist in AI Foundation Model Infrastructure, targeted for graduates in 2027. Responsibilities include designing scalable infrastructure for model training and optimizing distributed systems. Candidates should be pursuing a degree in computer science or related fields and possess solid coding skills in C/C++ or Python. The position offers a salary range of $202160 to $368220 annually, alongside comprehensive health benefits and personal time off.

Qualifications

• Currently pursuing a degree in computer science, mathematics, engineering, or a related field with expected graduation in 2027.
• Excellent coding ability in C/C++ or Python with strong algorithm skills.
• Demonstrated interest or project experience in relevant areas.

Responsibilities

• Design and build scalable infrastructure for large-scale model training.
• Optimize distributed training systems across compute, memory, and communication.
• Improve system reliability and efficiency for large-scale workloads.
• Develop frameworks for evaluation and model lifecycle management.
• Co-design systems to enhance performance of foundation models.

Skills

C/C++

Python

Data structures

Algorithms

Problem-solving

Collaboration

Education

Bachelor's or Master's degree in relevant field

Job description

Research Scientist in AI Foundation Model Infrastructure - Seed - Graduates - 2027 Start (BS/MS)

Location: Seattle

Team: Technology

Employment Type: Regular

Job Code: A238983

Responsibilities

• Design and build scalable infrastructure for large-scale model training, evaluation, and inference.
• Optimize distributed training systems across compute, memory, and communication.
• Improve system reliability, efficiency, and observability for large-scale workloads.
• Develop frameworks for evaluation, data processing, and model lifecycle management.
• Co-design systems and algorithms to improve performance of foundation models.

Qualifications

Minimum Qualifications

• Currently pursuing a Bachelor's or Master's degree in computer science, mathematics, engineering, or a related field, with an expected graduation date in 2027 and the ability to commit to an onboarding date by the end of 2027.
• Excellent coding ability, data structures, and fundamental algorithm skills, proficient in C/C++ or Python, etc.
• Demonstrated interest or project experience in relevant areas.

Preferred Qualifications

• Experience in distributed systems, machine learning systems, or related areas through internships is preferred.
• Strong problem-solving and collaboration skills.

Base salary range for this position in the selected city is $202160 - $368220 annually.

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).

Equal Employment Opportunity (Los Angeles County)

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:

• 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.