ML Engineer, LLM & Multimodal Systems

Bytedance; San Jose, Northern (CA, KY); Hybrid; USD 120,000 - 170,000; Full time

Job summary

ByteDance's Applied Machine Learning Ark team in San Jose seeks engineers and researchers to advance LLM MaaS platforms. You will work across model serving, evaluation, and data pipelines, collaborating with cross-functional teams to deploy reliable AI services.

Candidates should have a strong foundation in deep learning and Python, with familiarity in PyTorch and multimodal AI. You will contribute to end-to-end systems spanning training, inference, and evaluation in a fast-growing tech

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, ML, CV or related field.
• Strong Python skills and experience with PyTorch or equivalent framework.
• Experience with deep learning and computer vision fundamentals.
• Experience with multimodal evaluation or related frameworks.

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

Python programming

Deep learning basics

Multimodal evaluation

Research/engineering ability

Education

Bachelor's or Master's degree in Computer Science, AI, ML, CV or related field

Tools

PyTorch

Job description

ByteDance's Applied Machine Learning Ark team in San Jose seeks engineers and researchers to advance LLM MaaS platforms. You will work across model serving, evaluation, and data pipelines, collaborating with cross-functional teams to deploy reliable AI services.

Candidates should have a strong foundation in deep learning and Python, with familiarity in PyTorch and multimodal AI. You will contribute to end-to-end systems spanning training, inference, and evaluation in a fast-growing tech