Senior Machine Learning Engineer

Role overview A remote (USA) position focused on building AI-driven components that automate security analysis and deliver high-fidelity risk identification across customer data. The team’s mission is to surface every threat while keeping false positives low, blending machine learning, data engineering, and threat research. You will turn risk-identification methods into agent-based logic and own the path from research to production.

Responsibilities - Convert risk-identification approaches into agent logic, ensuring quality across risk analysis, explanations, and recommendations. - Partner closely with threat-research to understand adversary data, attack techniques, and the heuristics used to detect them. - Define data requirements and collaborate with data engineering on pipelines, enrichments, and aggregations that feed detection models. - Drive a data-driven quality bar for threat detection, including backtesting, precision/recall tuning, and ongoing quality control. - Deploy and monitor models and agents in production through a CI/CD framework, with reliability practices such as structured logging, metrics, and alerting.

Requirements - Hands-on experience building LLM-powered agents in production, including tool/function calling, prompt and context engineering, multi-step orchestration (e.g., LangGraph, LangChain, or equivalent), and evaluation against ground truth. - 3+ years of professional Python development with production-quality system design, validated inputs, data contracts, and unit/integration testing. - Strong SQL skills and experience with Python data-analytics libraries such as pandas, Polars, or NumPy. - Demonstrated ownership of production reliability in a live distributed system: CI/CD, containerization, observability, on-call participation, and incident debugging. - Familiarity with AWS and infrastructure-as-code practices. - Senior-level ownership and leadership of projects, development, and testing.

Nice to have - Experience with production AI/ML systems where cost, latency, and accuracy are jointly optimized. - Background working with data pipelines, schema evolution, backfills, and data quality validation.

Benefits and work setup - Remote (USA) role. - Base pay range: $115,500–$165,000 USD (excluding commission, bonus, and equity, plus benefits). - Health plan options, vacation and sick time, parental leave, retirement options, and education reimbursement, plus in-office perks.