Senior AI/ML Engineer for Hazard Detection & Analytics

Consumer Product Safety Commission; Washington (District of Columbia); Remote; USD 144,000 - 187,000; Full time

Benefits offered by this job

Paid vacation

Sick leave

11 paid holidays per year

Life and health insurance

Long Term Care insurance

Dental and vision insurance

Flexible spending account

Retirement plan (Thrift Savings Plan)

Job summary

The Senior AI/ML Engineer at the Consumer Product Safety Commission leads the design and deployment of AI/ML models to detect hazards and improve data-driven decisions. The role emphasizes safe cloud-native workflows, scalable compute, and robust model governance.

Responsibilities include building advanced models (NLP, deep learning, agents), integrating AI into risk-reduction workflows, and collaborating across programs to deliver operational solutions with transparency and reliability.

Qualifications

• Bachelor's degree or higher in mathematics, statistics, computer science, data science or closely related field; degree must be commensurate with the position.
• Combination of education and experience with 30 semester hours in the major field; transcripts required.

Responsibilities

• Design, develop, test, and deploy supervised and unsupervised ML models including NLP, deep learning, and agents.
• Operationalize models in secure cloud environments (Azure preferred) with containerization and CI/CD.
• Lead data pipeline design, data ingestion, transformation, validation, and quality enforcement.

Skills

ML modeling

Data pipelines

Cloud computing

Python

Education

Bachelor's degree or higher in mathematics, statistics, computer science, data science

Tools

PyTorch

TensorFlow

Azure

CI/CD

Job description

The Senior AI/ML Engineer at the Consumer Product Safety Commission leads the design and deployment of AI/ML models to detect hazards and improve data-driven decisions. The role emphasizes safe cloud-native workflows, scalable compute, and robust model governance.

Responsibilities include building advanced models (NLP, deep learning, agents), integrating AI into risk-reduction workflows, and collaborating across programs to deliver operational solutions with transparency and reliability.