Scientific Computing Engineer, Digital Pathology

Description Are you motivated to participate in a dynamic, multi-tasking environment? Do you want to join a company that invests in its employees? Are you seeking a position where you can use your skills while continuing to be challenged and learn? Then we encourage you to dive deeper into this opportunity.

We believe in career development and empowering our employees. Not only do we provide career coaches internally, but we offer many training opportunities to expand your knowledge base! We have highly competitive benefits with a variety HMO and PPO options. We have company 401k match along with an Employee Stock Purchase Program. We have tuition reimbursement, leadership development, and even start employees off with 16 days of paid time off plus holidays. We offer wellness courses and have highly engaged employee resource groups. Come join the Neo team and be part of our amazing World Class Culture!

NeoGenomics is looking for a Scientific Computing Engineer who wants to learn to continue to learn in order to allow our company to grow. This is an onsite position in Aliso Viejo, CA with a Monday - Friday, day shift.

Now that you know what were looking for in talent, let us tell you why youd want to work at NeoGenomics: As an employer, we promise to provide you with a purpose driven mission in which you have the opportunity to save lives by improving patient care through the exceptional work you perform. Together, we will become the worlds leading cancer reference laboratory.

Position Summary: As a Scientific Computing Engineer you will lead infrastructure performance for NeoGenomics on-premise high-performance computing (HPC) and scientific storage environments and optimizes the edge-to-core data lifecycle to ensure massive volumes of Whole Slide Imaging (WSI) and NGS data flow seamlessly from laboratory instruments to compute resources and long-term storage. You will fine-tunes network throughput, storage latency, and compute job scheduling to ensure the physical laboratory infrastructure functions as a high-velocity launchpad for downstream AI analysis and hybrid cloud processing.

Responsibilities:

• Designs, deploys, and maintains on-premise high-performance computing clusters and scientific storage solutions (e.g., high-throughput NAS, object storage) tuned for large-scale biomedical data
• Optimizes the write path for critical lab instruments (digital pathology scanners, NGS sequencers), ensuring network and storage configurations prevent data bottlenecks during high-volume acquisition
• Architects automated data lifecycle policies to move data efficiently between hot local processing tiers, warm on-premise retention, and cold cloud archival, balancing performance with cost
• Designs and maintains secure, high-bandwidth connectivity (e.g., Direct Connect, ExpressRoute) between on-premise infrastructure and cloud environments (Azure, AWS), partnering with Cloud DevOps teams to enable seamless hybrid workflows
• Monitors and tunes file system performance (I/O profiling), network latency, and job scheduler configurations to meet strict Service Level Agreements (SLAs) for lab turnaround times
• Leads technical evaluation, specification, and procurement of specialized compute hardware (GPU nodes for inference) and storage arrays to support growing data volumes
• Implements robust local backup and disaster recovery strategies for raw instrument data to ensure data integrity and business continuity prior to cloud replication
• Partners with Lab Operations and Engineering teams to troubleshoot complex hardware-software interaction issues at the instrument level
• Ensures all on-premise scientific infrastructure adheres to strict security standards (encryption at rest and in transit) and regulatory requirements (HIPAA, GxP)

Education, Experience & Qualifications:

• Bachelors degree in Computer Engineering, Electrical Engineering, or equivalent work experience required
• 12 or more years of experience in systems engineering with a strong focus on Linux administration and high-performance computing (HPC) environments required
• Manages petabyte-scale storage systems (NAS, SAN, Object Storage) and parallel file systems (e.g., Lustre, GPFS, Isilon, NetApp) in research or clinical settings
• Applies deep expertise in networking optimization for data-intensive workloads (10GbE/40GbE/100GbE, RDMA, TCP/IP tuning)
• Designs hybrid infrastructure connecting on-premise data centers to public cloud providers (Azure, AWS)
• Supports biomedical instrumentation, imaging pipelines, or genomics workflows
• Demonstrates infrastructure mastery with expert-level knowledge of Linux kernel tuning, storage I/O subsystems, and hardware architecture (CPU/GPU/RAM interactions)
• Automates system administration and data movement tasks using Python, Bash, or similar scripting languages
• Configures and manages job schedulers and workload managers (e.g., Slurm, LSF, Grid Engine) for efficient resource allocation
• Applies strong network engineering skills including switching, routing, and firewall configuration for high-throughput scientific data flows
• Uses a rigorous problem-solving approach to troubleshoot complex hardware and software bottlenecks in distributed systems

All qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status.