Perception MLOps Infrastructure Engineer
General Atomics Aeronautical Systems
•
Poway, CA
0
0
0
0
Entry level
Posted March 5, 2026
Job link
Thinking about this job
Responsibilities
Commitments
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
Commitments
Ability to obtain and maintain a DOD security clearance required.
Job Type: Full-Time Salary
Not Met Priorities
What still needs stronger evidence
Requirements
- 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.
- 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.
- Ability to obtain and maintain a DOD security clearance required.
Preferred Skills
- May substitute equivalent machine learning engineer experience in lieu of education.
- 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.
Education
- (Not 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.
- (Not required) – May substitute equivalent machine learning engineer experience in lieu of education.
Job Summary
General Atomics Aeronautical Systems, Inc. (GA-ASI), an affiliate of General Atomics, is a world leader in proven, reliable remotely piloted aircraft and tactical reconnaissance radars, as well as advanced high-resolution surveillance systems.
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.
This role bridges DevOps, software engineering, and systems integration to ensure scalable, reproducible environments for radar, EO/IR, and fused-sensor perception workloads executed on GPU-based platforms across simulation, lab, and flight environments.
DUTIES AND 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
We recognize and appreciate the value and contributions of individuals with diverse backgrounds and experiences and welcome all qualified individuals to apply.
Job Qualifications
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.
Desired Qualifications
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.
Soft Skills and Team Fit
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.
Ability to obtain and maintain a DOD security clearance required.
Job Type: Full-Time Salary
Salary range: 81,080 - 141,650
General Atomics Aeronautical Systems, Inc. (GA-ASI), an affiliate of General Atomics, is a world leader in proven, reliable remotely piloted aircraft and tactical reconnaissance radars, as well as advanced high-resolution surveillance systems.
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.
This role bridges DevOps, software engineering, and systems integration to ensure scalable, reproducible environments for radar, EO/IR, and fused-sensor perception workloads executed on GPU-based platforms across simulation, lab, and flight environments.
DUTIES AND 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
We recognize and appreciate the value and contributions of individuals with diverse backgrounds and experiences and welcome all qualified individuals to apply.
Job Qualifications
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.
Desired Qualifications
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.
Soft Skills and Team Fit
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.
Ability to obtain and maintain a DOD security clearance required.
Job Type: Full-Time Salary
Salary range: 81,080 - 141,650