About the Role: - Design and build machine learning systems for real-time fraud detection. - Work across machine learning, backend engineering, and data infrastructure. - Own end-to-end ML solutions from data processing to production deployment. - Build scalable, reliable, and efficient systems for high-stakes fraud prevention. Responsibilities: - Build and optimize real-time data pipelines for device and behavioral data. - Develop, deploy, and maintain production ML models for fraud detection. - Engineer production-ready features from raw data. - Collaborate with backend and platform engineers to integrate ML systems. - Monitor model performance and iterate based on production feedback. - Maintain security, privacy, and compliance standards. - Follow best practices for testing, documentation, and observability. Required Qualifications: - Experience: 5–8 years of software engineering experience. - Strong backend engineering experience with Go or Python. - Hands-on experience building applied machine learning solutions using large datasets. - Experience owning end-to-end ML systems, including feature engineering and pipelines, model deployment, production monitoring, and continuous model improvement. - Experience building latency-sensitive ML systems serving real-time predictions at scale. - Strong backend engineering mindset with production system experience. - Experience in fraud detection, including areas such as bot detection, device fingerprinting, and VPN and proxy detection. Technical Skills: - Go or Python. - PyTorch or Scikit-learn. - SQL. - Relational and NoSQL databases. - Feature pipelines. - Model monitoring and drift detection. - Docker. - Kubernetes. Education: - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Soft Skills: - Strong written and verbal English communication. - Self-directed and comfortable working with minimal supervision. - Able to navigate ambiguity and deliver independently. Preferred Qualifications: - Stronger backend engineering background than data science. - Experience with CI/CD pipelines. - Knowledge of browser APIs and high-entropy data collection techniques. - Experience using frontier LLMs for automation. - Ability to quickly learn new programming languages. Compensation: - Salary: $175K–$220K. - Competitive equity. Employment Details: - Full-time. - Remote-first within the United States or Canada. - Hiring 3 candidates. Locations: - New York. - San Francisco. - South Bay Area. - Los Angeles. Visa Eligibility: - No H-1B sponsorship. - TN visa supported. - L-1 and O-1 considered on a case-by-case basis. Tech Stack: - Go. - Python. - SQL. - Docker. - Kubernetes.

Machine Learning Engineer

Emma

Requirements and responsibilities

Skills wanted:

Language(s) required:

Muhammad Saleem Khan Verified

Muhammad Saleem Khan Verified

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