Job Information
Fusion Risk Management Machine Learning Engineer in US, United States
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 reinforcement learning and optimization-driven intelligence-to power the next generation of resilience capabilities.
You will architect and deliver scalable ML systems that unify resilience data from some of the world's largest and most systemically important organizations. This includes building robust model pipelines, integrating simulation and optimization engines into production services, and establishing strong ML Ops and AI Ops practices to ensure reliability, performance, 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 AI/ML infrastructure, ship intelligent product features, and solve complex real-world operational resilience challenges.
Key Responsibilities
Design, build, deploy, and maintain production machine learning systems, including reinforcement learning components and intelligent optimization-driven features.
Architect scalable ML pipelines for training, validation, deployment, monitoring, and automated retraining.
Maintain and expand operations for simulation (Monte Carlo, Bayesian Networks) and optimization engines (linear, constraint, CP-SAT) for continued reliable service.
Own ML Ops and AI Ops practices, including CI/CD for models, automated testing, model validation, performance monitoring, drift detection, observability, and governance frameworks.
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.
Contribute to the design of Fusion's ML architecture, infrastructure standards, and long-term intelligent systems roadmap.
Knowledge, Skills, and Abilities
Strong software engineering foundation with hands-on experience building and deploying machine learning systems in production environments.
Experience designing ML architectures, APIs, and services that integrate with enterprise SaaS platforms.
Deep understanding of model lifecycle management: experimentation, validation, deployment, monitoring, retraining, and versioning.
Experience with reinforcement learning, decision systems, simulation modeling, or optimization techniques.
Strong experience building scalable data and feature pipelines using cloud-native tools (e.g., Azure, Snowflake, dbt, Salesforce integrations, or similar platforms).
Proficiency in writing clean, maintainable, well-tested code with version control, CI/CD, and observability best practices.
Familiarity with containerization and distributed systems (Docker, Kubernetes, serverless architectures, or similar).
Ability to design modular, extensible ML systems that evolve alongside product requirements.
Strong communication skills and the ability to explain system behavior, tradeoffs, and architectural decisions to technical and non-technical stakeholders.
Qualifications (Education and Experience)
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Engineering, or a related field.
3+ years of experience building, deploying, and operating machine learning systems in production environments.
Experience with reinforcement learning, decision intelligence systems, or control systems (strongly preferred).
Experience with simulation, optimization, constraint programming, or operations research techniques (preferred).
Experience building ML pipelines in cloud environments (Azure preferred).
Experience implementing ML Ops tooling for testing, validation, monitoring, retraining, and governance (preferred).
Experience deploying AI-powered systems within enterprise SaaS environments (nice to have).
Milestones for the First Six Months
In One Month, You Will:
Complete onboarding and gain familiarity with Fusion's resilience domain, existing product line, simulation and optimization engines.
Contribute code to existing ML systems and participate in production improvements.
Review and assess current ML pipeline and deployment practices.
In Three Months, You Will:
Design and deploy at least one production-ready ML component or reinforcement learning module.
Improve reliability, performance, or scalability of existing intelligent systems.
Implement monitoring, validation, and automated testing for one production AI/ML system.
In Six Months, You Will:
Own and deliver a production-grade intelligent capability (e.g., adaptive optimization engine, reinforcement-driven decision module, or production-trained GPT workflow).
Establish baseline ML Ops standards for model deployment, monitoring, retraining, and governance.
Lead architectural improvements to Fusion's ML infrastructure.
Propose and prototype new ML-driven product capabilities that extend Fusion's resilience intelligence platform.
Compensation & Benefits
The annual base salary range for this position is $135,000-$155,000, depending on experience, qualifications, and relevant skill set. The position is also eligible for an annual bonus. Fusion offers a comprehensive benefits package including medical, dental, vision, and a 401(k) plan.
Disclaimers
Fusion is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, age, pregnancy, military service or discharge status, genetic information, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.