Machine Learning Engineer

As a Machine Learning Engineer, you'll do more than build models—you'll design the systems that make fraud detection possible. You'll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale.

This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.

· Build and optimize data pipelines and backend services to process device and behavioral data in real time.

· Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.

· Turn raw data into production-ready features that feed fraud detection systems.

· Collaborate with platform and backend engineers to integrate models seamlessly.

· Maintain high standards of security, privacy, and compliance.

· Champion best practices in testing, documentation, and observability.

· 5+ years in software engineering, with strong backend experience (Go or Python).

· Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).

· Strong SQL skills and familiarity with relational and non-relational databases.

· Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration.

· Excellent communication skills in English, both written and verbal.

· Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline.

· Domain knowledge in fraud, risk, or cybersecurity.

· Familiarity with CI/CD, Docker, Kubernetes, and modern DevOps frameworks.

· Understanding of modern browser APIs and high-entropy data collection techniques.

· Familiarity with leveraging frontier LLMs for automation.

· $175K – $220K

· Competitive equity

· US or Canada-based, remote-first.

· Offices available in:

o Bay Area

o NYC

o Austin

o Toronto

o São Paulo

· Visa sponsorship not available.

· TN: OK

· L1/O1: Case-by-case

· No H1-B sponsorship.

· Full-time

· Looking to hire 3 candidates for this role.

· New York

· San Francisco

· South Bay Area

· Los Angeles

· Go

· Python

· SQL

· Docker

· Kubernetes

Required:

· 5–8 years of experience in software engineering with strong backend and machine learning work.

Required:

· End-to-end ML model ownership: feature pipelines, model deployment, monitoring, and iteration (not just experimentation).

· Fraud domain experience (bot detection, device fingerprinting, VPN/proxy detection, etc.).

Nice to Have:

· Depth in backend software engineering over data science.

Required:

· BS or MS in Computer Science, Engineering, or a related field.

Required:

· Built latency-sensitive ML systems serving real-time predictions at scale.

· Familiarity with ML platform tooling: feature pipelines, drift monitoring, model iteration cycles.

Nice to Have:

· Experience with Go for backend services (or demonstrated ability to pick up new languages quickly).

Required:

· Self-directed: navigates ambiguity and delivers with minimal hand-holding after onboarding.