Job Title

Senior Machine Learning Engineer IRemote or Hybrid within OR, WA, ID or UTCambia Health Solutions is working to create a seamless and frictionless healthcare experience for consumers nationwide. This presents a unique challenge and opportunity for innovative solutions that serve patients and providers and influence the healthcare system. Cambia's AI team builds, prototypes, and deploys data-driven models and algorithms to production systems, delivering more equitable, effective, and affordable health care to our members. We are seeking a highly-skilled and experienced Senior Machine Learning Engineer to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better health care. We contribute broadly across Cambia, working on a wide range of challenging problems, for instance: Reducing our members' claim costs using both supervised and unsupervised approaches. Speeding up prior authorizations and appeals using NLP to understand clinical notes. Personalizing member engagement to promote the health and well-being of our members. Driving health equity across Cambia initiatives. And much more! As a Senior ML Engineer on our team, you will play a crucial role in identifying gaps in our existing ML platform and architecting and building solutions to address those gaps. You will also collaborate with the AI team's ML Scientists and our partner data engineering and software development teams to bring ML models to production and maintain their health and integrity while in production. Your expertise in machine learning, coupled with a strong background in software development, will be instrumental in driving the success of Cambia's AI/ML initiatives. Qualifications and Requirements: Academic degree (Masters or PhD preferred) in Computer Science, Engineering, or a related field. Minimum of 7 years of experience in ML development and deploying ML solutions in cloud-based production environments. Experience with ML platforms and ML Ops: Demonstrated experience in assessing and improving ML platforms, identifying gaps, and architecting solutions to address them. Strong familiarity with ML platform components such as data ingestion, preprocessing, feature stores, model training, deployment, and monitoring. Strong machine learning expertise: Proficient in machine learning algorithms, statistical modeling, and data analysis. Hands-on experience with standard ML frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, XGBoost). Software development skills: Solid understanding of software engineering principles, data structures, and algorithms. Proficient in Python and/or other programming languages commonly used in ML development. Data preprocessing: Proficient in SQL and/or python data-processing libraries such as pandas, NumPy, etc. Collaborative mindset: Ability to work effectively with cross-functional teams to drive alignment and deliver high-quality solutions. Strong communication and collaboration skills are essential. Problem-solving and analytical thinking: Ability to analyze complex problems, break them down into manageable components, and propose innovative solutions. Strong analytical skills for evaluating system performance, identifying bottlenecks, and implementing optimizations. Continuous learning: Passion for staying up-to-date with the latest advancements in machine learning and data engineering. Proactive in learning new tools, techniques, and frameworks to drive innovation and improve the ML platform. Healthcare knowledge: Previous experience is beneficial but not required.Responsibilities: ML platform and ML Ops: Identify areas that require improvements or additional functionalities and use your expertise in machine learning and software engineering to architect and develop solutions that fill gaps in our ML platform and development ecosystem. Analyze system performance, scalability, and reliability to pinpoint opportunities for enhancement. Develop tools and solutions that help the team build, deploy, and monitor AI/ML solutions efficiently. System scalability and reliability: Optimize the scalability, performance, and reliability and AI Team solutions by implementing best practices and leveraging industry-standard technologies. Collaborate with infrastructure teams to ensure smooth integration and deployment of ML solutions. Design scalable and efficient systems that leverage the power of machine learning for enhanced performance and capabilities. Data processing and workflow pipelines: Streamline data ingestion, preprocessing, feature engineering, and model training workflows to improve efficiency and reduce latency. Work with data engineering and data platform teams to design and implement robust data pipelines that support the AI team's needs. Model deployment and monitoring: Evaluate and optimize model prototypes for real-world performance. Work with infrastructure and development teams to integrate ML models into production systems. Work closely with partner teams to communicate and understand technical requirements and challenges. ML advancements: Keep abreast of the latest trends, research, and advancements in the field of machine learning. Share knowledge and insights with the team to drive innovation and continuous improvement within the ML platform. Work Environment: Work primarily performed in a hybrid environment consisting of in-office and working from home. Travel may be required, locally or out of state. "