Agentic Systems Research Engineer for Multimodal Video AI

Doist; New York (NY); On-site; USD 140,000 - 230,000; Full time

Benefits offered by this job

Medical, dental, and vision

401K with match

Commuter benefits

Catered lunch

Dinner stipend / Grubhub

Wellness perks

Team offsites

Generous PTO

Job summary

Mirage is seeking an ML Engineer to push the boundaries of large language models for multimodal creative tasks. You'll develop new approaches for building and extending agentic systems that understand and operate over complex, real-world data, particularly video.

This role focuses on advancing agent capabilities, improving reasoning and control, and enabling new forms of interaction between language models and time-based media.

Qualifications

• BS/MS/PhD in CS, ML, or related field.
• Proven track record building production ML systems.
• Strong understanding of transformers and modern LLM techniques.
• Experience with fine-tuning, alignment, or post-training methods, especially for adapting models to generate structured outputs or drive tool use.

Responsibilities

• Design and build end-to-end agentic systems for creative tasks.
• Develop novel approaches for training and adapting the large language models that power these agents.
• Design new objectives, datasets, and fine-tuning strategies to improve agent behavior and reliability.
• Explore multimodal reasoning and structured generation for creative control.
• Run systematic experiments to evaluate and improve agent performance in real-world tasks.
• Design evaluation frameworks for agentic workflows in video analysis and editing.
• Analyze failure modes across the full agent loop (planning, tool use, execution) and iterate on improvements.

Skills

Production ML systems

Agentic pipelines

Transformers / LLMs

Experimental rigor

Education

BS/MS/PhD in CS, ML, or related field

Job description

Mirage is seeking an ML Engineer to push the boundaries of large language models for multimodal creative tasks. You'll develop new approaches for building and extending agentic systems that understand and operate over complex, real-world data, particularly video.

This role focuses on advancing agent capabilities, improving reasoning and control, and enabling new forms of interaction between language models and time-based media.