Senior - Staff HPC Engineer

A pioneering AI-driven biotech organization is seeking a Principal Engineer to lead the development and optimization of its GPU-based computing infrastructure. This role is central to enabling large-scale training of deep learning models that power breakthroughs in biological research and healthcare innovation.

You'll be responsible for architecting and managing high-performance GPU clusters, implementing distributed training strategies, and collaborating with research teams to integrate scalable infrastructure into model development workflows. This is a high-impact role at the intersection of AI, biology, and systems engineering.

Key Responsibilities

• Design, deploy, and maintain high-performance GPU clusters, ensuring reliability, scalability, and efficient resource utilization.
• Implement distributed and parallel training techniques to accelerate deep learning model convergence across multi-GPU and multi-node environments.
• Optimize system performance through profiling, tuning, and analysis of deep learning workloads.
• Collaborate with AI researchers and ML engineers to integrate distributed training capabilities into model development pipelines.
• Develop strategies for resource allocation and prioritization to support growing computational demands.
• Troubleshoot and resolve issues related to cluster performance, training workflows, and infrastructure anomalies.
• Maintain clear and comprehensive documentation for cluster configurations, workflows, and operational best practices.
• Ensure compliance with data privacy and security standards, particularly in handling sensitive biological data.

Ideal Qualifications

• Master's or PhD in Computer Science, High-Performance Computing, Distributed Systems, or a related field.
• 2+ years of hands-on experience managing GPU clusters, including setup, configuration, and performance optimization.
• Deep expertise in distributed deep learning and parallel training methodologies.
• Proficiency in frameworks such as PyTorch, Megatron-LM, DeepSpeed, and GPU-accelerated libraries like CUDA and cuDNN.
• Strong programming skills in Python, with experience in system-level optimization.
• Familiarity with profiling tools and performance tuning for HPC and deep learning environments.
• Experience with resource schedulers and orchestration tools (e.g., SLURM, Kubernetes).
• Background in cloud computing (AWS, GCP) and containerization (Docker, Kubernetes).

This role offers the opportunity to shape the future of AI-powered biological discovery by building the infrastructure that enables cutting-edge model development. If you're passionate about high-performance systems and want to contribute to transformative science, we'd love to hear from you.