Applied AI Research Engineer – Multimodal & Production

Agentio; New York (NY); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Flexible PTO

Health coverage

Dental & Vision plans

401(k) Retirement Plan

Lifestyle credit $100 monthly

Lunches and snacks in office

Dinner provided Mon-Thu

Wellhub gold access

Gym in the building

Job summary

Agentio is seeking a Research Engineer to advance applied AI/ML research that improves the platform’s predictions and decision-making for creator advertising.

You’ll evaluate methods from academia and industry, adapt models on Agentio’s data, and ship production-ready solutions with engineering and product teams.

The role spans research and deployment, requiring strong judgment, hands-on ML skills, and the ability to turn complex problems into practical systems.

Qualifications

• Demonstrated experience conducting applied ML research with production ML systems.
• PhD or advanced degree in ML/AI/CS is useful but not required.
• Depth in foundation models, multimodal learning, ranking or RL.
• Ability to turn ambiguous product problems into tractable research questions.
• Strong engineering skills and experience taking ML work beyond prototypes.
• Strong knowledge of current AI research and practical methods.

Responsibilities

• Own research problems from problem definition to production.
• Evaluate AI/ML methods against Agentio’s data with rigorous experiments.
• Develop models, algorithms, training approaches, or datasets and ship them.
• Help set Agentio’s research agenda and standards for applied ML.

Skills

Applied ML research

Production ML systems

Research judgment

Engineering skills

Knowledge of AI research

Education

PhD or advanced degree in ML/AI/CS

Job description

Agentio is seeking a Research Engineer to advance applied AI/ML research that improves the platform’s predictions and decision-making for creator advertising.

You’ll evaluate methods from academia and industry, adapt models on Agentio’s data, and ship production-ready solutions with engineering and product teams.

The role spans research and deployment, requiring strong judgment, hands-on ML skills, and the ability to turn complex problems into practical systems.