Senior Machine Learning Engineer

About This Role

As a Senior Machine Learning Engineer, you will build, deploy, and optimize machine learning services for the Tebra platform. This hands-on role focuses on ensuring the reliability, speed, and scalability of AI solutions, taking ownership of model pipelines to make them efficient, testable, and maintainable.

Responsibilities

• Write high-quality, production-grade software for data ingestion, feature extraction, and model inference, optimizing for latency, throughput, and resource efficiency
• Implement robust CI/CD pipelines, automated testing, and comprehensive logging/monitoring for deployed models
• Construct and maintain specific data pipelines required for training and inference, ensuring data quality and consistency
• Develop reusable software modules and utilities that streamline the development process, championing clean code and test-driven development
• Translate business requirements into technical specifications and execute them with precision, breaking down complex tasks into deliverable units
• Monitor daily performance of production models, debug incidents, and execute routine retraining workflows to address data drift
• Partner with Engineering team members and Product Managers to estimate effort, flag technical risks, and deliver features on schedule

Requirements

• 5+ years of professional software development experience including system design, large-scale services, and production-grade infrastructure
• 3+ years of hands-on experience in machine learning engineering or applied AI, with a strong record of deploying and maintaining models in production
• Technical subject matter expertise in 3+ general areas of software development including machine learning infrastructure
• Demonstrated ability to deliver significant, measurable real-world impact through applied ML
• Proven ability to design and write modular, performant, and easy to read software that solves complex business problems
• Proficiency in Python, TensorFlow/PyTorch, and scikit-learn
• Strong background in MLOps and data infrastructure (e.g., Airflow, Spark, feature stores, MLflow, data versioning)
• Proven ability to deploy and maintain ML models in production with CI/CD, monitoring, and alerting
• Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization (Kubernetes, Docker)

Qualifications

• 5+ years of professional software development experience including system design, large-scale services, and production-grade infrastructure; 3+ years of hands-on experience in machine learning engineering or applied AI

Nice to Have

• Experience building or fine-tuning Large Language Models (LLMs) or generative models for structured business processes
• Experience with retrieval-augmented pipelines or feedback-driven model retraining
• Background in healthcare software operations, or financial automation
• Contributions to open-source ML infrastructure projects
• Published research or conference papers in machine learning, natural language processing, or applied AI
• Experience leading AI reliability and observability initiatives

Skills

* Required skills

Benefits