We're looking for a machine learning engineer to take models from notebook to production and keep them working once they're there. You'll build training pipelines, deploy models behind real APIs, and monitor for drift long after the initial launch excitement fades. Practical engineering skill matters as much as modeling chops, and you should be genuinely excited about the unglamorous work of monitoring and retraining, not just the initial build.
What you'll do
- Build and productionize machine learning models for core product features
- Design training pipelines and manage experiment tracking for reproducibility
- Deploy models as scalable services with monitoring for performance and drift
- Collaborate with data engineers on feature pipelines and data quality
- Evaluate model performance rigorously before and after deployment
- Partner with product to identify high-value applications for ML
What we're looking for
- 3+ years building and deploying machine learning models in production
- Strong Python skills including experience with PyTorch or TensorFlow
- Understanding of MLOps practices including monitoring and retraining
- Experience with cloud ML infrastructure (SageMaker, Vertex AI, or similar)
- Solid grasp of classical ML and, ideally, deep learning fundamentals
- Comfortable working closely with data engineering on pipeline dependencies
Nice to have
- Experience with large language models or generative AI applications
- Background publishing or contributing to ML research
- Familiarity with feature stores and real-time inference systems
About Meridian West
About Meridian West: Meridian West provides payment and treasury infrastructure trusted by enterprise finance teams to move money reliably at scale. Headquartered in New York, the company combines regulatory, risk, and engineering expertise to support clients processing billions of dollars in annual volume across marketplaces, payroll, and insurance.
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