Senior Machine Learning Engineer

📋 Description

• • As a Senior Machine Learning Engineer at Tebra Inc., you will play a pivotal role in shaping the future of our platform by building, deploying, and optimizing the machine learning services that are integral to Tebra's operations. This is a hands-on, deeply technical position where you will be the primary architect and builder of robust ML subsystems, translating complex, high-level requirements into production-ready, scalable, and efficient code.
• • Your core responsibility will be to ensure the reliability, speed, and scalability of our AI solutions. You will take direct ownership of specific model pipelines, meticulously ensuring they are not only performant but also easily testable and maintainable in a production environment. This involves a deep dive into the entire ML lifecycle, from data ingestion and feature engineering to model inference and ongoing monitoring.
• • You will be tasked with writing high-quality, production-grade software. This includes developing sophisticated code for data ingestion, feature extraction, and model inference, with a sharp focus on optimizing for critical performance metrics such as latency, throughput, and resource efficiency. The goal is to deliver ML services that are both powerful and cost-effective.
• • Implementing robust CI/CD pipelines is a key aspect of this role. You will establish and maintain automated testing frameworks, comprehensive logging mechanisms, and real-time monitoring systems for the models you deploy. This proactive approach is crucial for the immediate detection and resolution of any potential issues, ensuring uninterrupted service.
• • Constructing and maintaining specific data pipelines is essential for both training and inference. You will ensure the integrity, quality, and consistency of data at the component level, which directly impacts the accuracy and effectiveness of our ML models.
• • A significant part of your contribution will involve developing reusable software modules and utilities. These assets will streamline the development process for the entire team, fostering efficiency and consistency. You will champion best practices such as clean code principles and test-driven development (TDD) to elevate the overall quality of our codebase.
• • You will act as a bridge between business needs and technical execution. This involves translating business requirements into precise technical specifications and then executing them with a high degree of accuracy. Your expertise in breaking down complex, multifaceted tasks into manageable, deliverable units will be invaluable.
• • Continuous monitoring of production models is a daily responsibility. You will track performance metrics, debug incidents swiftly, and execute routine retraining workflows to counteract data drift and maintain model accuracy over time. This ensures our ML systems remain relevant and effective.
• • Collaboration is key. You will partner closely with other Engineering team members and Product Managers. This includes providing accurate effort estimations, proactively flagging technical risks, and ensuring the timely delivery of features that align with product roadmaps.
• • This role offers the opportunity to make a significant, measurable real-world impact through applied machine learning. You will be instrumental in driving innovation and enhancing Tebra's offerings through intelligent solutions.
• • You will leverage your technical subject matter expertise across multiple areas of software development, including machine learning infrastructure, to design and implement modular, performant, and easily readable software that addresses complex business challenges.
• • Your proficiency in Python, along with deep experience in ML frameworks like TensorFlow/PyTorch and scikit-learn, will be critical. A strong background in MLOps and data infrastructure, encompassing tools such as Airflow, Spark, feature stores, MLflow, and data versioning, is also expected.
• • You will be responsible for deploying and maintaining ML models in production, utilizing CI/CD practices, and implementing effective monitoring and alerting systems. Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization technologies like Kubernetes and Docker is essential.
• • Experience in building or fine-tuning Large Language Models (LLMs) or generative models for structured business processes, as well as working with retrieval-augmented pipelines or feedback-driven model retraining, will be highly valued.
• • Finally, you will bring excellent technical communication skills and a strong product mindset, demonstrating comfort in driving initiatives from initial concept through to successful delivery.