Machine Learning Engineer - Richmond
Core
Design, build, and deliver ML models and components to address real business problems, guiding infrastructure decisions and automating testing/deployment.
Role type
Machine Learning Engineer
Builds
Production ML models, optimized data pipelines, and cloud-based architectures
Domain
Financial services / AI & Machine Learning
Deliverable
production ML models
Required skills
Python, Java, Golang, C++, PyTorch, TensorFlow, Pandas, NumPy, Scikit-learn, Spark, Ray, AWS, GCP, Azure, Kubernetes, CI/CD, model training, hyperparameter tuning, data pipeline design
Preferred skills
ML algorithm tuning, software development best practices (source control, testing, CI/CD), resilient software solution building, model evaluation and diagnosis, academic publication, large-scale data pipeline scaling
Technologies
AWS, Azure, GCP, Kubernetes, Spark, Ray, PyTorch, TensorFlow, Python, Java, Golang, Scala, Pandas, NumPy, Scikit-learn
Responsibilities
Design and build ML models to solve business problems; guide ML infrastructure decisions including model choice and feature selection; write and test application code; collaborate on cross-functional Agile teams; retrain, maintain, and monitor production models; develop optimized data pipelines; apply CI/CD best practices for reliable deployment; ensure code governance and Responsible AI practices.
Seniority
Mid-Senior, hands-on IC
