Senior Machine Learning Engineer (AI)

TL;DR

Senior Machine Learning Engineer (AI): Building and optimizing distributed deep learning pipelines for large-scale genomic data with an accent on scalability, hardware efficiency, and infrastructure robustness. Focus on designing reliable AI systems, optimizing model training workflows, and collaborating with cross-functional research teams to accelerate cancer detection technology.

Location: Must be based in the US; hybrid option available at the Brisbane, California headquarters.

Salary: $161,925–$227,325

Company

hirify.global is a biotech company mission-driven to reduce cancer mortality through early detection using massive-scale genomic data.

What you will do

• Develop and deploy infrastructure to support deep learning models, including distributed pipelines and model optimization.
• Collaborate with scientists and software engineers to align infrastructure with operational research needs.
• Monitor and optimize training pipeline performance for scalability and hardware efficiency.
• Maintain reproducible DL pipelines to ensure consistency and accuracy of scientific results.
• Bridge the gap between engineering and research teams by documenting best practices and driving cross-functional communication.
• Benchmark and profile systems to accelerate model training and evaluation cycles.

Requirements

• 5+ years of industry experience building AI/ML software engineering pipelines.
• Proficiency in Python and hands-on experience with frameworks like PyTorch or TensorFlow.
• Deep knowledge of distributed computing platforms such as Ray or DeepSpeed.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker and Kubernetes.
• Strong background in managing large-scale datasets and distributed data processing.
• Must be authorized to work in the United States.

Nice to have

• Experience working with large-scale genomic or biological datasets.
• Knowledge of GPU/accelerator programming (CUDA, Triton, or XLA).
• Experience with infrastructure-as-code and MLOps best practices.
• Contributions to open-source DL projects on GitHub.

Culture & Benefits

• Equity and cash bonus eligibility for all positions.
• Comprehensive medical, financial, and wellness benefits.
• Hybrid work environment with a collaborative, interdisciplinary research culture.
• Commitment to diversity and equal opportunity employment.