Principal Systems Software Engineer

About this role

Principal Systems Software Engineer

Location: San Diego, CA

Job Type: Full-Time

Salary Range: $258,000 - $275,000

Position Overview

We are looking for a Principal Engineer to architect, build, and own the end-to-end data pipeline that drives our high-throughput diagnostic

instrument platform — from real-time image acquisition on the instrument, through GPU-accelerated signal processing, to offloading

for secondary and tertiary analysis on local HPC clusters and cloud infrastructure.

This is a technical leadership role for an engineer who can design and deliver industrial-grade data processing infrastructure that operates

reliably at sustained high throughput. You will be responsible for the full data path: acquiring raw image data from sensors, processing it

through GPU pipelines, orchestrating job distribution across local HPC and cloud compute, and ensuring the entire system handles errors,

backpressure, and recovery gracefully. The scope spans instrument- embedded software, on-premises Linux HPC infrastructure, and cloud-

based compute and storage.

The central challenge of this role is not raw compute optimization — GPU and CPU resources will have adequate headroom. The challenge is

building a pipeline architecture that is robust, scalable, and evolvable as instrument throughput increases with each generation, the number

of instruments grows, and data volumes scale accordingly. You will design systems that keep a complex multi-stage pipeline running

continuously and reliably in a production lab environment, and that can be evolved without wholesale re-architecture as requirements

intensify.

Key Responsibilities

End-to-End Data Pipeline Architecture

- Own the architecture of the complete data path from image acquisition to final processed output

- Design pipeline stages with clear interfaces, flow control, and backpressure mechanisms

- Ensure the pipeline sustains continuous high-throughput operation across extended instrument runs

- Define data formats, handoff protocols, and buffering strategies between pipeline stages

- Architect for graceful degradation — the system must handle transient failures without data loss or pipeline stalls

- Establish performance budgets and SLAs for each pipeline stage and monitor adherence

Image Acquisition & On-Instrument Processing

- Develop and optimize real-time image acquisition from high-speed sensors on the instrument

- Implement low-latency, high-bandwidth data capture with minimal frame loss

- Design on-instrument preprocessing stages that reduce data volume before offload

- Manage memory and storage constraints within the instrument compute environment

- Ensure deterministic, repeatable performance under sustained acquisition loads

GPU-Accelerated Signal & Image Processing

- Develop and maintain GPU compute pipelines using CUDA for signal and image processing

- Implement DSP algorithms including frequency-domain analysis, deconvolution, filtering, and detection

- Manage host-to-GPU data transfers and ensure efficient use of GPU resources

- Profile GPU workloads to identify issues and validate performance headroom

- Balance numerical accuracy against throughput requirements

Job Orchestration & Distributed Processing

- Design and implement job queuing, scheduling, and orchestration across instrument, local HPC, and cloud compute

- Build robust work distribution that maximizes resource utilization across heterogeneous compute

- Implement backpressure handling so upstream stages throttle gracefully when downstream is saturated

- Design comprehensive error handling, retry logic, and dead-letter strategies for failed jobs

- Ensure jobs are idempotent and recoverable — partial failures must not corrupt the pipeline

- Implement priority scheduling to balance real-time instrument processing with batch reprocessing

- Monitor queue depths, processing latencies, and resource utilization with actionable alerting

Linux Systems & Performance

- Configure and tune Linux systems for reliable, high-throughput operation across instrument and HPC nodes

- Tune kernel parameters (scheduler, NUMA, IRQs, huge pages) as needed for stable pipeline performance

- Understand and manage DMA paths, PCIe topology, and device-to- memory data movement

- Profile and diagnose system-level issues using perf, ftrace, eBPF, and similar tools

- Ensure system configurations are reproducible and documented across instrument and HPC environments

HPC Compute Platform & Algorithm Infrastructure (co- owned with DevOps)

- Co-design the HPC compute platform architecture with DevOps — define computational requirements, job flow, and data access patterns while DevOps provisions and manages the infrastructure

- Define how algorithms are deployed, versioned, and rolled into production on the HPC platform — support safe side-by-side execution of new and existing algorithm versions

- Design compute allocation strategies that balance real-time instrument processing, batch algorithm development/validation, and historical data reprocessing

- Design the data handoff between instrument-side processing and

- HPC/cloud compute — formats, staging, transfer protocols

- Define storage tiering requirements for the processing pipeline — what data stays hot for active processing, what moves to warm for algorithm development access, and what archives to cold

- Specify when and how workloads should burst from local HPC to cloud (AWS) based on pipeline load and priority

- Optimize data movement across high-speed networks (RDMA,

- InfiniBand, high-speed Ethernet) between instrument, HPC, and storage

- Design for scalability — the architecture must accommodate increasing instrument throughput, additional instruments, and growing algorithm complexity

Reliability & Observability

- Instrument every pipeline stage with metrics, logging, and tracing

- Build real-time dashboards showing pipeline health, throughput, la

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