Multimodal Vision ML Engineer – Gen & Evaluation

ByteDance; San Jose (CA); On-site; USD 128,000 - 256,000; Full time

Benefits offered by this job

Medical insurance (health)

Dental insurance

Vision insurance

401(k) with company match

Parental leave

Disability insurance

Life insurance

Wellbeing benefits

Holidays & PTO

Job summary

ByteDance’s Applied Machine Learning Ark team develops and operates large-scale LLM service platforms and MaaS solutions, spanning text and multimodal models, training, inference, and evaluation. The team also runs log analytics pipelines to extract usage patterns and generate insights for model and product improvements.

We invite graduates and researchers to join, with onboarding by year-end. Applicants may apply to up to two positions; applications are reviewed on a rolling basis across our

Qualifications

• Bachelor's/Master's degree in Computer Science, AI/ML, Computer Vision or related field.
• Strong foundation in deep learning and computer vision, including generative modeling fundamentals.
• Experience in visual generation, multimodal LLMs, video understanding, or visual quality assessment.

Responsibilities

• Build evaluation systems for image and video models/agents, covering generation quality, instruction following, multimodal understanding, and safety.
• Develop automated metrics and model-based evaluators, and design reproducible human evaluation protocols.
• Design and develop video generation/debugging agents that orchestrate multi-step creative workflows.
• Build large-scale image and video data pipelines, and turn evaluation findings into model and product improvements.

Skills

Deep learning

Computer vision

Python

PyTorch

Research ability

Multimodal evaluation

Education

Bachelor's/Master's degree in CS/AI/ML/vision

Tools

PyTorch

Job description

ByteDance’s Applied Machine Learning Ark team develops and operates large-scale LLM service platforms and MaaS solutions, spanning text and multimodal models, training, inference, and evaluation. The team also runs log analytics pipelines to extract usage patterns and generate insights for model and product improvements.

We invite graduates and researchers to join, with onboarding by year-end. Applicants may apply to up to two positions; applications are reviewed on a rolling basis across our