Senior Machine Learning Engineer, Tebra

• 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 (e.g., server, database, security, etc) 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)
• 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
• Excellent technical communication and a product mindset—comfortable driving initiatives from concept to delivery

Desirable

• As a Senior Machine Learning Engineer, you will build, deploy, and optimize the machine learning services that power the Tebra platform
• You will be the primary builder of robust ML subsystems, translating high-level requirements into production-ready code
• This is a hands-on role where you will focus on the reliability, speed, and scalability of our AI solutions
• You will take ownership of specific model pipelines, ensuring they are efficient, testable, and maintainable
• Write high-quality, production-grade software for data ingestion, feature extraction, and model inference, specifically focusing on optimizing code for latency, throughput, and resource efficiency
• Implement robust CI/CD pipelines, automated testing, and comprehensive logging/monitoring for the models you deploy to ensure immediate detection of issues
• Construct and maintain specific data pipelines required for training and inference, ensuring data quality and consistency at the component level
• Develop reusable software modules and utilities that streamline the development process for the wider team, while championing clean code and test-driven development
• Translate business requirements into technical specifications and execute them with precision, serving as an expert at breaking down complex tasks into deliverable units
• Monitor the 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