Principal AI Engineer - Risk Foundations (Remote)

Plata Card; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Relocation support to hubs

Flexible work from offices or remote

Healthcare coverage

Education budget

Wellness budget

Vacation policy

Job summary

Plata is seeking a Principal AI Engineer to lead end-to-end foundation models for financial event data and drive production-grade ML systems that influence risk and decisioning metrics. You will own model research direction, development, and deployment, working with vast datasets across customers and transactions.

The role requires deep learning on sequential data, strong mathematical background, and experience taking models to production, with collaboration across product, risk, and leadership

Qualifications

• Proven experience applying deep learning to sequential data.
• Experience taking large models to production: distributed training & serving.
• Strong mathematical or statistical foundation.
• Clear communication with cross-functional teams.

Responsibilities

• Build end-to-end foundation models for financial event sequences (transactions, bureau data, behavioral events).
• Drive methodology, implementation, and production performance with latency and cost considerations.
• Mentor engineers; set standards for model development and deployment.
• Communicate results to product, risk, business, and leadership.

Skills

Deep learning

Sequential data modeling

Transformer architectures

Production ML

Distributed training

Model serving

Communication

Education

MS/PhD in CS math or related

Job description

Plata is seeking a Principal AI Engineer to lead end-to-end foundation models for financial event data and drive production-grade ML systems that influence risk and decisioning metrics. You will own model research direction, development, and deployment, working with vast datasets across customers and transactions.

The role requires deep learning on sequential data, strong mathematical background, and experience taking models to production, with collaboration across product, risk, and leadership