As a Staff AI Engineer, you will take end-to-end ownership of machine learning systems deployed in real-world healthcare settings. Your work will center on building robust, observable, and maintainable AI platforms that operate at scale, ensuring performance, reliability, and compliance with operational standards.
Key Responsibilities
- Manage the complete machine learning lifecycle—from data pipelines and model training to deployment, monitoring, retraining, and eventual deprecation.
- Transform research prototypes into production systems using automated CI/CD workflows, infrastructure-as-code, and scalable cloud architectures.
- Lead architectural discussions and set best practices for ML systems, MLOps, and AI infrastructure across engineering teams.
- Design and maintain inference services and AI-driven applications with a focus on efficiency, scalability, and cost-effectiveness.
- Integrate large language models, generative AI, and NLP techniques to process unstructured clinical text and deliver actionable insights.
- Implement comprehensive monitoring, alerting, and logging to detect model drift, ensure data quality, and uphold service-level agreements.
- Build and optimize automated pipelines for continuous integration and delivery, leveraging containerization and orchestration tools like Docker and Kubernetes.
- Apply cloud-native principles on AWS or Azure to deploy and manage resilient, secure, and highly available environments.
- Guide junior engineers, promote code quality, and lead efforts to reduce technical debt across AI systems.
- Investigate and resolve production issues with a focus on long-term stability and system resilience.
- Champion non-functional requirements such as scalability, maintainability, and fault tolerance in system design.
- Work closely with Product, Data Science, and Engineering teams to align AI capabilities with business objectives and clinical use cases.
Qualifications
You bring 8+ years of experience in software or ML engineering with a track record of delivering and operating production AI systems. You hold a degree in Computer Science or a related field, or have equivalent professional experience. Your background includes deep technical expertise in software engineering fundamentals, distributed systems, and cloud infrastructure.
Proficiency in Python or Java is essential, with a strong commitment to writing clean, testable, and maintainable code. You have hands-on experience deploying ML models using MLOps platforms such as MLflow, SageMaker, or Kubeflow, and are skilled in containerization and infrastructure automation.
Familiarity with NLP, embeddings, and vector databases like Elasticsearch is required. Experience with generative AI, LLMs, and agentic workflows in production is a key differentiator. You are comfortable working in cross-functional, distributed teams and can clearly communicate technical trade-offs to diverse stakeholders.
Preferred Background
- Experience in healthcare or clinical AI applications.
- Knowledge of modern ML frameworks including PyTorch, TensorFlow, or Hugging Face.
- AWS certification in Machine Learning or Solutions Architecture.
Technology Environment
Our stack includes Python, Java, AWS, Azure, Docker, Kubernetes, MLflow, SageMaker, Kubeflow, Elasticsearch, Hugging Face, PyTorch, TensorFlow, CI/CD pipelines, infrastructure-as-code, and vector databases. We emphasize automation, observability, and scalable design patterns across our AI platform.
Compensation & Benefits
Base salary is determined by role level, candidate experience, skills, and geographic location, with potential adjustments based on these factors. The total compensation package may include performance bonuses or incentives. A comprehensive benefits program supports your health, wellness, and long-term goals.
