Applied AI Researcher | Plurall AI

Visa sponsorship: Available.
Compensation: Discussed during the hiring process.

About Plurall

Plurall AI builds technology to help businesses detect synthetic media and combat digital fraud. Its work spans deepfake detection, media forensics, and verification.

We’re a New York-based team of five, led by repeat founders, with a flat culture and direct collaboration across research, engineering, and product.

We’re looking for an AI researcher who combines rigorous research with strong engineering execution. You should be comfortable reading and writing research papers, designing experiments, and turning promising ideas into working code that can reach customers.

What you’ll do

• Investigate methods relevant to deepfake detection, synthetic media analysis, and fraud prevention.
• Read, evaluate, and reproduce relevant research, identifying approaches worth developing further.
• Design experiments, establish baselines, and evaluate model performance on realistic data.
• Implement research ideas and work with engineering to bring successful approaches into production.
• Analyze failure cases and improve model reliability, generalization, and computational efficiency.
• Document methods and findings, and contribute to research papers and technical writing.
• Work with the founders to prioritize research that addresses practical customer problems.

What you bring

• Strong foundations in machine learning, experimental design, and model evaluation.
• Demonstrated ability to translate research ideas into working software.
• Experience building, training, and evaluating models using modern ML tools.
• The ability to read technical papers critically and communicate your own methods and findings clearly.
• Evidence of research and engineering ability through shipped systems, publications, open-source contributions, or substantial applied projects.
• Comfort taking ownership of open-ended problems within a small team.

Nice to have
Experience in computer vision, audio processing, multimodal learning, media forensics, or adversarial robustness would be particularly relevant. Experience deploying models under real-world latency and reliability constraints is also valuable.

Why join
You’ll help shape the research direction, contribute directly to the product, and see your work applied to practical problems in digital trust and security.