ML Engineer

Role Description

We’re hiring an ML Engineer to join Kodex’s Verifications / Threat Intel org and help build the intelligence layer that protects our platform — detecting suspicious activity, improving verification accuracy, and turning messy real-world signals into scalable, auditable systems.

This role sits at the intersection of product, data, and security. You’ll partner closely with Threat Intel operators and engineers to translate investigative workflows into production-grade models, pipelines, and decision-support tooling. You’ll also help evolve our current approach beyond noisy, brittle heuristics toward systems that are measurable, explainable, and safe to operate.

What You’ll Do

• You’ll work across the full lifecycle — from problem framing and data exploration through deployment, monitoring, and iteration. Example areas include:
• Build and deploy models to detect and flag suspicious behavior (classification, anomaly detection, clustering, ranking, and other approaches as appropriate)
• Own ML pipelines end-to-end (feature generation, training, evaluation, batch/streaming inference, backfills, and versioning)
• Design evaluation + monitoring: ground truth strategies with Threat Intel, offline metrics, drift monitoring, and alerting
• Collaborate with product & engineering to integrate intelligence into user-facing workflows (e.g., review queues, and decision-support tooling)
• Improve data quality and accessibility by working with data engineering patterns (schemas, lineage, reproducibility, and access controls)
• Contribute to operational rigor: runbooks, incident response, and safe rollout practices for systems that impact customer trust

Qualifications

• 4+ years of experience building software ML systems in production
• Experience in fraud, abuse, trust & safety, risk, or security analytics
• Experience with modern ML/DS workflows: experimentation, evaluation, and deploying models that other teams rely on
• Comfortable with data pipelines and messy real-world data (instrumentation gaps, bias, label noise, and changing definitions)
• Think carefully about reliability and safety (rollbacks, guardrails, human-in-the-loop review, and audit-ability)
• Communicate clearly across disciplines and can turn ambiguous investigative needs into well-scoped engineering work
• Pragmatic: know when to start with a baseline and when to invest in stronger modeling/infra

Requirements

• Familiarity with LLMs / RAG / agentic workflows for internal tooling or decision support
• Experience in compliance-sensitive environments (privacy, access controls, and audit trails)

Benefits

• Remote-first within the U.S.
• Biannual offsites in exciting locations. Past trips include Seattle, Miami, Nashville, and San Francisco
• Competitive salary and meaningful equity
• Unlimited PTO + 14 company holidays
• 12 weeks of fully paid parental leave, with a flexible return-to-work policy
• Comprehensive medical, dental, and vision plans
• 401(k) retirement plan