Machine Learning Engineer (MLE) - Open to Remote

As a Machine Learning Engineer at Triumph Intelligence, you'll be part of a distributed team dedicated to productionizing and scaling ML systems that power the future of logistics.

You'll work closely with our EU-based ML Engineering team and US-based Data Science team to deploy, optimize, and maintain ML models that solve real-world freight challenges at scale.

Your primary focus will be production ML engineering: deploying models to production, building and maintaining ML infrastructure, automating ML pipelines, optimizing model performance for latency and throughput, and monitoring production systems. You'll also participate in model development and refinement alongside the Data Science team. Your work ensures high availability, performance, and reliability of our ML applications while

continuously applying software engineering best practices.

The ML Engineering role at Triumph combines production deployment, infrastructure engineering, pipeline automation, and model development. You'll work with Data Scientists and the EU ML team to integrate ML systems into our logistics platform. You'll also serve as a technical bridge to US stakeholders, translating production ML metrics and system behavior into clear business insights when needed.

This is a US-based remote position requiring core working hours on Eastern Standard Time (EST) to ensure a 3-4-hour daily overlap with our EU-based ML team.

What You'll Do

The MLE role at Triumph Intelligence focuses on productionizing models, building infrastructure, and maintaining production systems. In this role, you'll be expected to perform ML engineering activities including:

• Deploy and productionize ML models developed by the Data Science teams.

• Build and maintain production ML infrastructure with automated pipelines, monitoring, alerting, and CI/CD practices to ensure high availability and reliability.

• Develop and integrate complex business logic in Python to embed models into production systems and workflows.

• Scale and optimize model performance and serving infrastructure (latency, throughput, caching, quantization) to meet strict production SLAs.

• Construct and optimize data pipelines that feed production ML systems.

• Collaborate with US stakeholders to translate production metrics and system behavior into business insights when needed.

What We're Looking For

• Strong communication skills with the ability to explain the "why" behind technical decisions to diverse audiences.

• 4+ years of software engineering experience building and maintaining production ML systems.

• Proficiency with ML frameworks (scikit-learn, LightGBM, XGBoost).

• Strong production systems knowledge: Docker, Kubernetes, AWS (S3, ECS/EKS, SageMaker).

• Experience building and optimizing data pipelines at scale using ETL tools, and workflow orchestration platforms (Prefect, Airflow).

• Strong testing and CI/CD practices (automated testing, deployment pipelines).

• Monitor and maintain production ML systems with alerting, logging, and retraining workflows.

• Ability to work core hours on Eastern Standard Time (EST) to facilitate a 3-4 hour daily overlap with our EU-based engineering team.

Bonus Points For

• Leadership experience in engineering teams or technical consulting.

• Contributions to open-source ML projects.

• Experience optimizing and monitoring model behavior using sophisticated. hyperparameter tuning methods and specialized anomaly detection techniques.

• Previous work in the logistics or supply chain domain.

• Published work or presentations on ML engineering topics.

Join Us

If you're passionate about building scalable AI/ML systems and want to shape the future of freight ("physical internet"), we'd love to hear from you!