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The Role

We’re looking for a product-minded Machine Learning Engineer to pioneer the
engineering of intelligent resilience systems at Fusion. This role will focus on designing,
building, deploying, and operating production-grade machine learning systems—
including predictive models, reinforcement learning, and optimization-driven
intelligence—to power the next generation of resilience capabilities.

A core focus of this role is building ML systems that get smarter over time. Fusion’s data
strategy centers on three proprietary feedback loops—predictive threat intelligence,
threat escalation prediction, and ML-powered recovery modeling—where customer
outcomes flow back to retrain and improve models continuously. You will own the
infrastructure that makes these flywheels work: model evaluation, automated retraining,
CI/CD for models, drift detection, and governance at scale.

This is a high-ownership role for someone who thrives at the intersection of software
engineering and machine learning—someone who wants to build durable ML
infrastructure, ship intelligent product features, and ensure that production models are
rigorously evaluated, reliably deployed, and continuously improved.

Key Responsibilities

• Design, build, deploy, and maintain production machine learning systems, including
predictive models for threat intelligence, escalation timing, and recovery prediction.

• Own the end-to-end model lifecycle for flywheel use cases: data ingestion, feature
engineering, training, rigorous evaluation, deployment, monitoring, and automated
retraining based on customer outcome data.

• Build and maintain robust model evaluation frameworks—including offline metrics,
A/B testing infrastructure, backtesting against historical outcomes, and calibration
analysis—to ensure models improve with each retraining cycle.

• Architect scalable ML pipelines with full CI/CD: automated testing of model code and
artifacts, validation gates before promotion, staged rollouts, and rollback capabilities.

• Own ML Ops and AI Ops practices, including automated model validation, performance
monitoring, drift detection, observability dashboards, and governance frameworks.

• Maintain and expand operations for simulation (Monte Carlo, Bayesian Networks) and
optimization engines (linear, constraint, CP-SAT) for continued reliable service.

• Design ML systems that operate across both managed cloud and customer-hosted
(reverse SaaS) environments, with pluggable inference adapters that respect customer
governance boundaries.

• Refactor and harden existing AI systems to improve scalability, latency, cost efficiency,
and fault tolerance.

• Build and maintain data pipelines and feature engineering workflows that support
reliable and reproducible model training.

• Collaborate closely with product and engineering teams to translate resilience use cases
into scalable, maintainable ML-powered product capabilities.