Machine Learning Engineer

Machine Learning Engineer — Fraud Detection & Real-Time ML Salary: $175K–$220K + Competitive Equity Location: Remote — US or Canada | Offices: Bay Area, NYC, Austin, Toronto, São Paulo Employment: Full-time Visa: No H-1B sponsorship | TN OK | L-1/O-1 case-by-case Tech: Go, Python, SQL, PyTorch, Scikit-learn, Docker, Kubernetes The Role We’re hiring Machine Learning Engineers to build and scale real-time fraud detection systems.

This is a hands-on engineering role combining applied machine learning, backend development, data pipelines, and production ML infrastructure. You’ll own ML systems end-to-end—from feature engineering and model development to deployment, monitoring, and optimization—serving latency-sensitive predictions at scale.

What You’ll Do

• Build real-time ML/data pipelines processing device and behavioral signals.
• Develop, deploy, and optimize fraud detection models in production.
• Build production-ready features from large datasets.
• Develop backend services and ML infrastructure using Go and/or Python.
• Integrate ML models with scalable backend and platform systems.
• Implement model monitoring, drift detection, testing, observability, and CI/CD.
• Design systems with strong security, privacy, and compliance standards.

Must-Have Qualifications

• 5–8+ years of software engineering experience with strong backend + ML experience.
• Proven experience owning end-to-end production ML systems: feature pipelines, deployment, monitoring, and iteration.
• Experience building latency-sensitive, real-time ML systems at scale.
• Hands-on applied ML experience with PyTorch, Scikit-learn, or similar frameworks.
• Strong Python and/or Go backend development skills.
• Strong SQL and experience with relational and NoSQL databases.
• Direct experience in fraud detection, bot detection, device fingerprinting, VPN/proxy detection, risk, cybersecurity, or related domains.
• BS/MS in Computer Science, Engineering, or a related technical field.
• Strong English communication and ability to work independently in ambiguous environments.

Nice to Have

• Go backend engineering experience.
• Docker, Kubernetes, CI/CD, and DevOps experience.
• ML platform tooling, feature stores/pipelines, drift monitoring, and model lifecycle management.
• Browser APIs and high-entropy data collection.
• Experience applying LLMs/AI automation to production systems.
• Background in fraud, fintech, payments, cybersecurity, or trust & safety.

Who This Is NOT For Not a pure Data Scientist, ML Researcher, or ML Ops-only role. We’re looking for engineers who can build, deploy, and operate production ML systems, with substantial backend engineering depth.

Ideal Backgrounds Candidates may come from fraud/risk, cybersecurity, fintech/payments, or trust & safety/applied ML environments, including companies such as Sift, Featurespace, Feedzai, Socure, Riskified, Unit21, Alloy, Palo Alto Networks, CrowdStrike, SentinelOne, Stripe, Block, PayPal, Plaid, Adyen, Brex, Google, Meta, Amazon, Apple, or Netflix.

Initial Screening Questions

1. What is your current US/Canada work authorization status?
2. What is your earliest available start date?
3. Have you built systems involving fraud detection, bot detection, device fingerprinting, VPN/proxy detection, or similar security/risk problems?
4. What are your salary expectations?
5. How actively are you exploring new opportunities?