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# ML Engineer, Agentic Systems

- **Company:** The Mirage
- **Location:** New York, NY, United States
- **Salary:** $175,000.0 - $275,000.0
- **Contract:** Permanent contract
- **Skills:** Language Modeling, Large Language Models, Multi-Agent Systems, Build Management, Machine Learning Operations
- **Published:** June 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ee3b36764ed9dab3

## About the Role

Do you have experience in Technical research projects?, Do you have a Bachelor's degree?, * BS/MS/PhD in CS, ML, or related field

 * Strong track record building production ML systems or agentic pipelines
 * Deep 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
 * Comfort owning the full stack, from model-level experiments to deployed agent systems
 * Strong experimental rigor and good taste for what makes agents actually work in practice

## 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.

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


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