Procurement Sciences is seeking a Machine Learning Engineer to build and scale classification and recommendation systems that power their core product, aiming to improve product performance through better recommendations, search quality, and user classification.
Requirements
- Strong foundation in traditional machine learning with production deployment experience
- 5+ years deploying ML models in production (classification, recommendations, or similar)
- Strong Python proficiency with ML libraries (scikit-learn, pandas, numpy) and deployment frameworks
- Experience with ML infrastructure: model serving, monitoring, and data pipelines
- Familiarity with foundation model APIs (OpenAI, Anthropic, etc.) and vector databases
- Track record of building systems that handle real user traffic and data
- Experience with LangChain, LangGraph, or similar LLM orchestration frameworks
Responsibilities
- Build, deploy, and maintain production classification and recommendation systems serving thousands of users
- Design and implement ML pipelines for training, evaluation, and monitoring of traditional ML models
- Integrate LLM APIs and vector databases into existing ML workflows to enhance product capabilities
- Collaborate with product and engineering teams to translate business requirements into scalable ML solutions
- Optimize model performance, system reliability, and inference latency across our ML stack
Other
- U.S. citizenship with the ability to pass a Federal Background Check and Identity Verification.
- While formal education is not a strict requirement, a Bachelor's or Master’s degree in Computer Science, Engineering, or a related field is preferred.
- Collaborative problem solver who thrives in startup environments with evolving requirements
- Eager to learn new technologies while leveraging proven ML engineering practices
- Background Check Required.
