ML Engineer - Verifications

Location

US-Remote

Employment Type

Full time

Location Type

Remote

Department

Software Engineering

Compensation

• Estimated base salary $150K – $180K • Offers Equity

About the company

Kodex revolutionizes how organizations handle sensitive subpoenas and data requests from law enforcement and government agencies. Founded by a former FBI agent and backed by leading investors including Andreessen Horowitz, Y Combinator, Peak XV and Thiel Capital, Kodex has become the industry standard for secure data exchange. Our platform supports over 15,000 government agencies in 190 countries and is trusted by industry leaders like Coinbase, Stripe, and AT&T. By transforming a traditionally complex and manual process into a streamlined digital workflow, Kodex helps organizations strengthen compliance, enhance security, and reduce operational costs by millions each year. We bridge the gap between companies and authorized requestors, ensuring that sensitive data is handled with uncompromising security, transparency, and efficiency.

About the Role

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,... access controls)
• Contribute to operational rigor: runbooks,... incident response,... safe rollout practices for systems that impact customer trust

You Might Be a Fit If You…

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

Bonus

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