Labcorp Genetics team is seeking an experienced Machine Learning Engineer to join their Applied AI team to solve complex business problems and develop production-ready ML systems that directly contribute to improving patient outcomes and advancing healthcare.
Requirements
- Proficiency across the technical stack, from infrastructure to application code, and in ML frameworks and training/deployment workflows. Technologies include: AWS, GCP, Azure, Python, FastAPI, OpenSearch, TensorFlow, PyTorch, Kubernetes, GitHub Actions, and New Relic.
- Experience with data preprocessing, feature engineering, model training, and model serving at production scale.
- Experience with LLMs, RAG, embeddings, vector search pipelines.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with MLOps best practices and tools for model versioning and deployment (data drift detection, canary/shadow deploys).
- Knowledge of genetics or experience in the healthcare domain.
Responsibilities
- Design, build, deploy, and monitor end-to-end ML solutions—feature pipelines, training workflows, real-time inference.
- Translate open-ended clinical and operational problems into clear ML approaches and success metrics.
- Lead the development of cloud-native infrastructure for secure, compliant, cost-effective model serving.
- Measure and improve model accuracy, latency, and reliability; integrate new research and user feedback.
- Partner with product managers, data scientists, and platform engineers; mentor teammates and set technical standards.
Other
- 10+ years of professional software or ML engineering experience (or equivalent demonstrable expertise, e.g. through advanced degree).
- Strong analytical and problem-solving skills, with a proven ability to create solutions with impact beyond your team.
- Able to drive and deliver cross-team or cross-discipline AI development projects on time and with high quality.
- Excellent written and verbal communication skills.
- Advanced degree in Computer Science, Machine Learning, or a related field.
