Machine Learning Engineer, Zus Health

• 3+ years of experience in machine learning, with a strong focus on model development and MLOps, including a proven track record of working with large language models (LLMs)
• Proficiency in programming languages such as Python, Java, or Go
• Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience with MLOps tools and platforms
• Familiarity with cloud services (e.g., AWS, GCP, Azure) and distributed computing
• Excellent analytical and problem-solving skills with a keen attention to detail
• Strong verbal and written communication skills. Ability to explain complex technical concepts to non-technical stakeholders
• Demonstrated ability to work effectively in a collaborative team environment

Desirable

• As a Machine Learning Engineer within the Data Acquisition (DA) Team, you will play a critical role in bringing your ML expertise to Zus
• The Data Acquisition team is responsible for building and running the microservices based infrastructure which connects with external health data networks to collect information about our patients and load it into the Zus data stores at high volume, as well as supporting those services used by customers and internal stakeholders to request that data
• You will be responsible for using your prior experience with large language models (LLMs) and MLOps to develop, deploy, and optimize solutions in collaboration with DA software engineering
• You will work closely within this cross-functional team to design, implement, and scale machine learning solutions that address key business challenges
• In your role as a ML Engineer, you will be responsible for conducting research to explore new methodologies and techniques, and integrating them into our product offerings
• You will develop prototypes to test and improve upon your innovations and develop feedback mechanisms to improve models with human oversight
• You will develop CI/CD pipelines, and automate workflows to ensure reliable and scalable model operations
• You will be responsible for presenting your learnings and helping the team leverage these methods and technique
• Product Innovation: engage cross functionally to discover opportunities to innovate better and more cost effective solutions for data collection, scaling, and normalizing
• Continuous Growth: Stay up-to-date with the latest advancements in machine learning and AI and figuring out how to leverage it
• Building the Right Tools: Choose the right LLMs, strategy for deployment and use; and work with the team to use them to make our Data Acquisition technology better
• Data Pipelines and Preparation: Collaborate with data engineers to build and maintain robust data pipelines. Ensure data quality and availability for training and evaluation purposes
• Machine Learning Ops: Implement and manage MLOps practices to streamline the deployment, monitoring, and maintenance of machine learning models in production
• Collaboration: Work closely with software engineers, product managers, and other stakeholders to understand requirements, provide technical expertise, and deliver high-quality solutions