Staff ML Engineer - Production Time-Series for MedTech

Oho Group; Los Angeles (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Oho Group is seeking a Staff Machine Learning Engineer to lead the development of production ML systems for connected-care products in a world-leading med-tech environment.

You will own end-to-end ML projects—from experimentation and model design to deployment, monitoring and adoption in real healthcare settings—while guiding architecture decisions and mentoring engineers across AWS, Docker and Kubernetes deployments.

Qualifications

• Production ML systems experience from research to deployment.
• Expertise in time-series forecasting, anomaly detection, or optimization.
• Excellent Python with PyTorch; strong Python ecosystem.
• Experience deploying ML on AWS with containers and CI/CD.

Responsabilités

• Build probabilistic and multi-series forecasting models for medication demand across products, locations and time horizons.
• Develop anomaly-detection systems for inventory, usage and operational patterns.
• Connect ML predictions with optimization methods to produce actionable recommendations.
• Evaluate classical forecasting methods against deep-learning and transformer-based architectures.
• Build agentic and natural-language interfaces to interrogate complex operational data.
• Own training pipelines, feature engineering, backtesting, calibration and shadow-mode evaluation.
• Deploy reliable ML services and batch workflows on AWS using Python, Docker and Kubernetes.
• Create monitoring for model quality, drift, latency and production behaviour.
• Develop explainable outputs for high-trust, regulated environments.
• Provide hands-on technical leadership through architecture decisions, code reviews and mentoring.

Connaissances

Time-series forecasting

Anomaly detection

Production ML systems

Python

PyTorch

Model deployment

Mentoring

Outils

Docker

Kubernetes

AWS

CI/CD

Description du poste

Oho Group is seeking a Staff Machine Learning Engineer to lead the development of production ML systems for connected-care products in a world-leading med-tech environment.

You will own end-to-end ML projects—from experimentation and model design to deployment, monitoring and adoption in real healthcare settings—while guiding architecture decisions and mentoring engineers across AWS, Docker and Kubernetes deployments.