Senior Machine Learning Engineer developing and integrating solutions for MoonPay's payments platform. Supporting fraud prevention and collaborating across teams in Fintech.
About the role
- Sr. AI/ML Engineer designing, developing, and deploying AI solutions for secure web applications at MetroStar. Collaborating with teams to support mission-focused environments.
Responsibilities
- Design, develop, implement, and fine-tune AI and machine learning models to support web-based applications in secure environments with evolving use cases.
- Build and maintain data pipelines, training workflows, and experimentation environments to enable rapid model iteration and evaluation.
- Evaluate model performance using quantitative and qualitative metrics (e.g., accuracy, robustness, stability, efficiency, generalization) and translate results into actionable improvements.
- Analyze data, model outputs, and experimental results to recommend changes to algorithms, features, data sources, or system architecture.
- Proactively identify and assess tools, frameworks, and technologies that best support platform goals, balancing performance, scalability, and maintainability.
- Collaborate closely with software developers, data engineers, DevSecOps teams, and stakeholders to integrate AI capabilities into production systems.
- Ensure AI and data science solutions are transparent, testable, and maintainable to support long-term operational use.
- Communicate technical approaches, assumptions, tradeoffs, and results clearly to both technical and non-technical audiences, including during design reviews and demonstrations.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical discipline.
- Active Secret security clearance (required).
- 4+ years of experience in data science, machine learning, or applied artificial intelligence.
- Strong hands-on experience developing, training, and tuning AI/ML models using Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
- Experience selecting and applying appropriate modeling approaches (e.g., supervised, unsupervised, reinforcement learning, or hybrid methods) based on problem context.
- Strong software engineering fundamentals including API design, clean architecture, testing, and Git-based workflows.
- Ability to work effectively in ambiguous problem spaces, defining requirements, success metrics, and implementation steps as understanding evolves.
- Experience integrating AI models into web-based or service-oriented platforms, working alongside DevSecOps engineering teams.
- Solid understanding of the full AI lifecycle, including data preparation, experimentation, validation, deployment considerations, and ongoing model improvement.
- Strong analytical and problem-solving skills, with sound judgment on when to prototype, iterate, or rethink an approach.
Benefits
- Health, dental, and vision insurance
- 401(k) retirement plan with company match
- Paid time off (PTO) and holidays
- Parental Leave and dependent care
- Flexible work arrangements
- Professional development opportunities
- Employee assistance and wellness programs
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