AI Engineer II

Ambry Genetics is seeking an experienced AI Engineer II to design, develop, and deploy scalable AI/ML solutions, with an emphasis on GPU-accelerated compute and cloud-native AI services. This mid-level role involves developing robust machine learning pipelines, optimizing model performance, and collaborating across R&D to drive biomedical innovation. You will contribute to both infrastructure and algorithm development for clinical diagnostics and multi-omics research. 

II. Duties and Responsibilities 

• Develop, optimize, and deploy AI/ML pipelines using GPU frameworks (e.g., CUDA, TensorFlow, PyTorch). 

• Build cloud-native AI services with scalable architecture using AWS or GCP. 

• Design APIs and workflows for real-time inference and offline batch processing. 

• Refactor and containerize AI services for production deployment (Docker, Kubernetes). 

• Evaluate model performance, reproducibility, and interpretability. 

• Collaborate with scientists, data engineers, and software developers to align infrastructure with experimental needs. 

• Participate in CI/CD pipeline development, Git-based workflows, and model lifecycle management. 

• Produce documentation, training materials, and perform code reviews to ensure quality and maintainability. 

III. Managerial Responsibilities 

This position does not include direct supervisory responsibility but may mentor junior engineers or interns. 

IV. Education and Knowledge 

• BS or MS in Computer Science, AI/ML, Software Engineering, or related discipline. 

• Strong foundational knowledge of deep learning, computer vision, or NLP. 

• Background in omics, computational biology, or biomedical data is a plus. 

V. Technical Competencies 

• Proficiency in Python and modern ML libraries (TensorFlow, PyTorch, HuggingFace, scikit-learn). 

• Hands-on GPU experience (NVIDIA CUDA, cuDNN) for model training and optimization. 

• Strong cloud engineering skills: AWS (EC2, S3, EKS, SageMaker), GCP, or Azure. 

• Full-stack software development preferred (React, Node.js, REST APIs). 

• Containerization, CI/CD: Docker, Kubernetes, Jenkins, GitHub Actions. 

• Familiarity with database systems: PostgreSQL, NoSQL, BigQuery. 

• Experience with structured/unstructured formats (JSON, XML, CSV) and web services. 

VI. Experience 

• 3+ years experience building ML systems or deep learning applications. 

• 1+ years in GPU-accelerated AI development or cloud-based AI deployment. 

• Prior contributions to production-grade AI pipelines or scientific platforms. 

• Experience in regulated or clinical environments (HIPAA, CLIA) is a bonus.