Machine Learning Engineer - Remote - United States

About the Role
As a Machine Learning Engineer, you'll design the systems that make fraud detection possible — working 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.

Compensation
Salary: $175K – $220K
Equity: Competitive equity
Visa Sponsorship: Not available (TN: OK, L1/O1: Case-by-case, No H1-B)

Work Arrangement
Full-time, remote-first (US or Canada-based)
Offices: Bay Area, NYC, Austin, Toronto, São Paulo
Locations: New York, San Francisco, South Bay Area, Los Angeles
Hiring Count: 3 openings

Key Responsibilities
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 reliability and efficiency 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

Mandatory Requirements
Seniority: 5–8 years of software engineering experience with strong backend (Go or Python) and ML work
Work Experience: End-to-end ML model ownership — feature pipelines, model deployment, monitoring, iteration (not just experimentation)
Fraud domain experience required (bot detection, device fingerprinting, VPN/proxy detection, etc.)
Hard Skills: Built latency-sensitive ML systems serving real-time predictions at scale
Familiarity with ML platform tooling: feature pipelines, drift monitoring, model iteration cycles
Hands-on applied ML with large datasets (PyTorch, Scikit-learn, etc.)
Strong SQL skills with relational and non-relational databases
Soft Skills: Self-directed — navigates ambiguity and delivers with minimal hand-holding
Communication: Excellent written and verbal English skills
Education: BS or MS in Computer Science, Engineering, or related field
Location: Must be based in US or Canada
Visa: TN or L1/O1 (case-by-case); no H1-B sponsorship

Bonus Points
Fraud, risk, or cybersecurity domain knowledge
CI/CD, Docker, Kubernetes, modern DevOps frameworks
Modern browser APIs and high-entropy data collection techniques
Leveraging frontier LLMs for automation
Experience with Go for backend services (or ability to pick up new languages quickly)

Tech Stack
Go, Python, SQL, Docker, Kubernetes

Traits to Avoid
Not just ML Ops — pure model-building with no production deployment or infrastructure experience
Depth in backend software engineering over data science preferred

Ideal Background
Fraud & Risk: Sift, Featurespace, Feedzai, Socure, Riskified, Unit21, Alloy, Vanta, Doppel
Cybersecurity: Palo Alto Networks, CrowdStrike, SentinelOne, Darktrace, Vectra AI
FinTech: Stripe, Block, PayPal, Plaid, Adyen, Brex, Taktile, Middesk, Flare
Trust & Safety: Google, Meta, Amazon, Apple, Netflix