AI Evaluation Engineer for LLMs & Multimodal Systems

ByteDance; San Jose (CA); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401(k) Savings Plan With Company Match

Paid Parental Leave

Short-term Disability Coverage

Long-term Disability Coverage

Life Insurance

Wellbeing Benefits

Paid Holidays

Paid Sick Days

Paid Personal Time

Job summary

ByteDance is seeking an AI Evaluation Scientist to build and refine evaluation technologies for large models in real-world business contexts. You will research state-of-the-art AI methods for LLMs, multimodal generation, and agent-based optimization, applying rigorous analysis to improve stability and efficiency.

The role emphasizes hands-on ML work, Python proficiency, and the ability to troubleshoot complex model applications within a fast-paced environment.

Qualifications

• Requires a Bachelor's or Master's degree in Computer Science or a related field.
• Hands-on experience with machine learning algorithms, Python proficiency, and troubleshooting large model applications.

Responsibilities

• Develop and implement evaluation technologies and frameworks for large models.
• Research AI methodologies for LLMs, multimodal generation, and agents to support model optimization.
• Apply analytical methods to improve stability and efficiency in real business scenarios.

Skills

Machine Learning

Deep Learning

Natural Language Processing

Python

Large Language Models

Evaluation Framework Design

Model Training

Model Deployment

Root-cause Analysis

Multimodal Understanding

Agents

World Models

Analytical Skills

Problem-solving

Communication

Teamwork

Education

Bachelor's or Master's degree in Computer Science or related field

Job description

ByteDance is seeking an AI Evaluation Scientist to build and refine evaluation technologies for large models in real-world business contexts. You will research state-of-the-art AI methods for LLMs, multimodal generation, and agent-based optimization, applying rigorous analysis to improve stability and efficiency.

The role emphasizes hands-on ML work, Python proficiency, and the ability to troubleshoot complex model applications within a fast-paced environment.