Machine Learning Engineer

About the role

MeeBoss is sharing this active opportunity on behalf of the hiring company. The organization is seeking an experienced Machine Learning Engineer to design and build production-grade machine learning systems that power real-time fraud detection. This role involves owning end-to-end ML solutions, from data pipelines and feature engineering to model deployment, monitoring, and backend infrastructure.

Why this role

• Opportunity to work on cutting-edge fraud detection technology.
• Collaborative engineering culture with significant ownership and impact.

Responsibilities

• Build and optimize data pipelines and backend services to process device and behavioral data in real time.
• Develop, deploy, and maintain machine learning models for fraud detection in production.
• Design and implement scalable feature pipelines from raw data.
• Collaborate with backend and platform engineering teams to integrate ML models into production systems.
• Monitor model performance, detect drift, and continuously improve model accuracy.
• Ensure high standards of security, privacy, reliability, and compliance.
• Follow engineering best practices for testing, documentation, and observability.
• Contribute to scalable backend services using Go and Python.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5–8 years of software engineering experience with strong backend development and applied machine learning.
• Experience building and deploying end-to-end ML systems, including feature engineering pipelines, model deployment, monitoring and drift detection, and continuous model improvement.
• Strong backend programming experience in Go or Python.
• Experience building latency-sensitive ML systems serving real-time predictions.
• Strong SQL skills and experience working with relational and NoSQL databases.
• Excellent written and verbal English communication skills.
• Ability to work independently in a fast-paced, remote environment.

Preferred qualifications

• Experience in fraud detection, risk, cybersecurity, bot detection, device fingerprinting, or VPN/proxy detection.
• Experience with Docker, Kubernetes, CI/CD pipelines, and modern DevOps practices.
• Familiarity with browser APIs and high-entropy data collection techniques.
• Experience using frontier LLMs to automate engineering workflows.
• Strong backend engineering background beyond traditional data science.

Benefits

• Competitive equity package.
• Fully remote (US or Canada).

Pay

$175,000–$220,000 per year

This response is AI-generated, for reference only.