Key skills
PythonEnd-to-end ML developmentDockerKubernetesSQLJupyter NotebooksML algorithmsMatplotlibSeabornAWS S3visualization skillsMathematical foundationsUnit testingcommunication skillsMachine LearningMLJupyterUnit TestingAWSS3Communication
About this role
Triumph is a company focused on creating a seamless freight transaction network. They are seeking an experienced Machine Learning Engineer to develop and optimize ML models that enhance logistics technology and ensure the efficiency of deployed models.
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
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
- 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