Staff Machine Learning Engineer

📋 Description

• • As a Staff Machine Learning Engineer at Tebra Inc., you will be instrumental in designing, developing, and deploying cutting-edge machine learning systems that form the core of our Tebra platform. This is a pivotal role that demands a blend of deep technical expertise and strong leadership, empowering you to drive innovation in applied ML within the healthcare technology sector.
• • You will take ownership of the entire machine learning lifecycle, from the initial stages of data exploration and rigorous model development to seamless production deployment, continuous monitoring, and iterative improvement. This end-to-end responsibility ensures that the ML systems you build are robust, scalable, and deliver tangible business value.
• • Your primary focus will be on designing, building, and operating highly scalable machine learning pipelines. These pipelines will encompass all critical stages: efficient data ingestion, sophisticated feature generation, robust model training, thorough evaluation, reliable deployment, and vigilant monitoring. This ensures a streamlined and automated workflow for ML operations.
• • You will be at the forefront of leveraging advanced machine learning techniques, including but not limited to gradient boosting methods and large language models (LLMs). These techniques will be applied to enhance automation and predictive capabilities across Tebra's core workflows, such as claims processing, payment systems, and billing operations, driving significant efficiency gains.
• • A key aspect of your role will involve conducting in-depth data analysis and designing rigorous experiments. This analytical approach will be crucial for identifying novel opportunities where ML can be applied to optimize processes, improve accuracy, and unlock new efficiencies within the platform.
• • Collaboration is central to success. You will work closely with cross-functional teams, including software engineering, product management, and other data-focused groups. This collaborative effort will ensure that AI capabilities are seamlessly integrated into Tebra's platform, aligning with product strategy and user needs.
• • You will play a critical role in establishing and championing best practices for model governance, ensuring reproducibility of experiments, enhancing model explainability, and implementing robust observability within the complex and regulated healthcare environment. This commitment to best practices will uphold the integrity and reliability of our ML systems.
• • As a Staff Engineer, you will also lead and mentor other engineers in the team. This involves sharing your expertise in applied ML methodologies, guiding them on system design principles, and fostering a culture of data-driven experimentation and continuous learning.
• • You will be responsible for continuously monitoring the performance of models deployed in production. This includes proactively detecting performance degradation or data drift and implementing automated retraining pipelines to maintain optimal accuracy and reliability over time, ensuring the systems remain effective.
• • The role requires a strong understanding of MLOps principles and data infrastructure. Experience with tools and platforms such as Airflow, Spark, feature stores, MLflow, and data versioning tools is essential for building and managing production-ready ML systems.
• • You will be expected to have a proven ability to deploy and maintain ML models in production environments, leveraging CI/CD practices, comprehensive monitoring, and effective alerting systems to ensure system uptime and performance.
• • Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization technologies like Kubernetes and Docker is crucial for deploying and scaling ML solutions effectively.
• • Experience in building or fine-tuning Large Language Models (LLMs) or other generative models for specific business processes will be highly valuable, enabling advanced natural language understanding and generation capabilities.
• • Experience with retrieval-augmented pipelines or feedback-driven model retraining mechanisms will be beneficial for enhancing model accuracy and adaptability.
• • While not strictly required, experience working with structured business or healthcare data would be advantageous, providing a deeper understanding of the domain.
• • Excellent technical communication skills are paramount, coupled with a strong product mindset. You should be comfortable driving initiatives from initial concept through to successful delivery, articulating technical concepts clearly to both technical and non-technical stakeholders.
• • A background in healthcare software operations or financial automation would be a significant plus, offering domain-specific insights that can accelerate project success.
• • Contributions to open-source ML infrastructure projects would demonstrate a commitment to the broader ML community and a deep understanding of foundational tools.
• • A track record of published research or conference presentations in machine learning, natural language processing, or applied AI would highlight expertise and thought leadership.
• • Experience leading AI reliability and observability initiatives, including the design of monitoring frameworks, drift detection systems, and alerting mechanisms for multiple production models, is highly desirable.