General Atomics Aeronautical Systems · 8 hours ago
Perception MLOps Infrastructure Engineer
Poway, CA
Full-time
Onsite
Senior Level
$81K/yr - $142K/yr
5+ years expGeneral Atomics Aeronautical Systems, Inc. (GA-ASI) is a world leader in proven, reliable remotely piloted aircraft and tactical reconnaissance radars. The Perception Infrastructure Engineer designs, builds, and maintains the compute, data, and CI/CD infrastructure that enables the development, test, and deployment of advanced perception algorithms for multi-sensor systems.
EnergyEnergy EfficiencyManufacturingWireless
No H1B
Security Clearance RequiredU.S. Citizen Only
Responsibilities
Architect, deploy, and maintain on-prem and isolated network compute infrastructure supporting perception algorithm development and test (GPU servers, storage arrays, and networked development hosts)
Design and manage GitLab CI/CD pipelines for build, test, and container deployment of perception software baselines (C++, CUDA, Python, ROS2)
Support algorithm developers with containerized and reproducible environments for ML training, sensor simulation, and embedded inference (Docker, Podman, Singularity)
Implement and maintain infrastructure-as-code for provisioning and configuration management (Ansible, Terraform, or equivalent)
Manage integration of data management tools (DVC, MLflow, Git LFS) for large datasets, model artifacts, and version tracking
Ensure secure network configuration and compliance with NIST SP 800-171 and corporate cybersecurity controls
Optimize GPU cluster scheduling and resource utilization (e.g., Slurm, Kubernetes, or GitLab runners for H100-class nodes)
Collaborate closely with perception algorithm engineers, autonomy software leads, and IT security to deliver reliable, high-throughput development pipelines
Support integration and test of perception software in hardware-in-the-loop and flight test environments
Qualification
Required
Typically requires a bachelors, masters degree or PhD in computer science, engineering, mathematics, or a related technical discipline from an accredited institution and progressive machine learning engineering experience as follows; five or more years of experience with a bachelors degree or three or more years of experience with a masters degree. May substitute equivalent machine learning engineer experience in lieu of education
Proficiency with Linux system administration, networking, and shell scripting
Experience with GitLab CI/CD or comparable build automation systems
Strong working knowledge of containerization (Docker/Podman) and environment reproducibility for development and deployment
Familiarity with GPU compute environments (CUDA drivers, Slurm scheduling, NVIDIA management tools)
Demonstrated experience maintaining source control and artifact management systems (Git, DVC, Artifactory)
Excellent documentation and troubleshooting skills across heterogeneous systems
Ability to obtain and maintain a DOD security clearance required
Preferred
Experience supporting AI/ML or perception pipelines for radar, EO/IR, or autonomy applications
Familiarity with C++, Python, and CUDA build environments
Experience in air-gapped or classified network environments
Knowledge of Kubernetes, MLflow, or Prometheus/Grafana monitoring
Understanding of DoD cybersecurity frameworks (RMF, NIST 800-171, STIG compliance)
Prior experience in aerospace, defense, or autonomy systems integration
Highly collaborative — able to work alongside algorithm developers, autonomy engineers, and IT security
Comfortable with rapid iteration, cross-functional coordination, and ownership of the end-to-end perception software lifecycle
System thinker with a bias for automation, reproducibility, and mission readiness
Company
General Atomics Aeronautical Systems
At General Atomics Aeronautical Systems, Inc.
5001-10000 employees
Funding
Current Stage
Late StageTotal Funding
$6.76MKey Investors
US Department of Energy
2023-08-14Grant
2023-05-09Grant
2022-09-19Grant
Leadership Team
Daniel Johnson
UK PROTECTOR Test Manager
Emory Lo
Sr Human Resource Business Partner
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