Machine Learning Engineer (MLE) - Open to Remote

Triumph Financial, Inc.

2d•Remote

About The Position

Join Triumph! At Triumph, our vision is a world where freight transactions are accurate and seamless on the most modern and secure freight transaction network. That’s why we’re looking for passionate, innovative, solutions-oriented people to join our team. We thrive on providing exceptional customer service and we look for team members with an entrepreneurial spirit and a passion to build successful partnerships with our clients. Because at the end of the day our goal is to help our partners businesses run better. We’re hiring experienced Machine Learning Engineer to join our growing team at Triumph Intelligence. As an ML Engineer, you will play a crucial role in advancing logistics technology by developing and optimizing ML models that address new business challenges in freight. You will be responsible for ensuring the efficiency and accuracy of our deployed models, scaling their performance, and automating ML pipelines. Your work will primarily focus on the end-to-end engineering of ML solutions, where you will build training infrastructure, optimize predictive models, and integrate complex business logic directly into our production processes. While the majority of your time will be spent owning features and delivering high-quality code alongside our EU-based team, you will also serve as a vital technical liaison for our US-based stakeholders. You will bridge the gap between technical execution and business strategy by de-mystifying model behaviors, explaining the "why" behind our roadmap, and ensuring our ML-driven solutions are both scalable and deeply aligned with real-world logistics challenges. This is a US-based remote position requiring core working hours on Eastern Standard Time (EST) to ensure a significant daily overlap with our EU-based team.

Requirements

Strong communication skills with the ability to explain the "why" behind technical decisions to diverse audiences.
4+ years of software engineering experience.
Deep knowledge of core tools including Python, SQL, Jupyter Notebooks.
Strong visualization and storytelling skills such as Matplotlib and Seaborn
End-to-end ML development and deployment experience.
Familiarity with ML algorithms and methodologies such as neural networks, time series, gradient boosting, and random forest.
Strong mathematical foundations include linear algebra, probability, statistics, and optimization.
Experience developing and deploying production-grade ML solutions using Docker, Kubernetes, AWS S3, and thorough unit testing.
Ability to work core hours on Eastern Standard Time (EST) to facilitate a 3–4 hour daily overlap with our EU-based engineering team.

Nice To Haves

Experience leading an engineering team or running consulting practice.
Active engagement with industry articles, research papers, and participation in competitions (e.g., Kaggle).
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.
Exposure to modeling market dynamics and economic trends.
Prior application of sophisticated hyperparameter tuning methods and specialized anomaly detection techniques.
Familiarity with AWS as a primary cloud platform for deploying and scaling ML workloads.

Responsibilities

Research and identify new business features to enhance prediction accuracy.
Monitor and maintain deployed ML models, ensuring accuracy and efficiency.
Automate ML pipelines and manage the entire model lifecycle.
Develop complex business logic in Python to integrate models into a company's processes.
Scale and optimize the performance of existing models (RPS, memory consumption) for strict latency requirements and high precision.
Translate complex ML roadmap items and model performance metrics into clear, actionable insights for non-technical stakeholders.