AI Engineer III-Remote

Ambry Genetics

13d•$130,000 - $170,000•Remote

About The Position

The AI Engineer III is a senior-level contributor responsible for designing, optimizing, and maintaining scalable AI infrastructure and GPU-accelerated model deployment environments. The ideal candidate will have deep experience with cloud-based ML systems, container orchestration, and data engineering in regulated clinical or bioinformatics settings. This role blends full-stack engineering with deep learning framework optimization and contributes to long-term AI platform strategy.

Requirements

BS/MS in Computer Science, AI/ML, Software Engineering, or related field (PhD preferred for research-heavy environments).
Deep understanding of GPU compute platforms (e.g., CUDA, cuDNN) and distributed training frameworks (e.g., Horovod, Ray).
Expert-level Python programming and strong experience with ML/DL libraries (PyTorch, TensorFlow, XGBoost, HuggingFace).
Strong knowledge of API development, cloud engineering (AWS/GCP), and MLOps tools (MLflow, Airflow, DVC).
Proven experience with Kubernetes, Docker, Terraform, and cloud orchestration.
Relational and NoSQL database expertise, data lake architecture (e.g., S3, BigQuery, Snowflake).
Experience with monitoring/observability tools (Prometheus, Grafana) and secure application deployment.
Minimum 5+ years in AI/ML engineering, with emphasis on production deployment and GPU acceleration.
At least 2 years of experience working in cloud-native environments and managing ML at scale.
Demonstrated track record of building AI systems for regulated or mission-critical applications.

Nice To Haves

Knowledge of biological data systems, genomics standards (VCF, FASTQ), and biomedical workflows is preferred.
Practical understanding of LLMs, generative models, and transformer architectures is a strong plus
Prior experience in bioinformatics or life sciences environments strongly preferred.

Responsibilities

Architect and optimize AI/ML systems for large-scale biomedical and omics applications using NVIDIA GPU frameworks.
Lead the development of scalable pipelines, APIs, and services for AI model training and real-time inference in cloud and hybrid environments.
Collaborate with cross-functional R&D and engineering teams to integrate AI models into clinical-grade software platforms.
Implement CI/CD pipelines, GitOps workflows, and model governance processes.
Contribute to data lake and data mesh strategies to enable AI scalability.
Provide technical leadership and mentorship to junior engineers and research scientists.
Ensure AI infrastructure and applications meet data integrity, security, and compliance standards (HIPAA, CLIA).
Other duties assigned.