below marketmost similar roles pay hereabove market
This role pays more than
85%
of similar roles. Most pay
$169,550–$214,500
— the blue band above.
At the midpoint, this role pays about
$242k
versus about
$192k
for comparable roles.
Based on 240 similar postings.
Employer
About SpaceX
SpaceX designs, manufactures, and launches advanced rockets and spacecraft with the mission of enabling humans to become a multi-planetary species. It operates the Falcon 9, Falcon Heavy, and Starship launch vehicles, as well as the Starlink satellite internet constellation.
SpaceX currently has
851 open roles
on FindRole.
Listed pay typically runs
$130,000–$165,000
across 590 roles with salary data.
The Principal Software Engineer (Platform Team) joins the team responsible for building foundational tooling and security infrastructure to enable effective AI use across the organization. This role involves architecting platforms that convert engineering know-how into automated systems, reliable data stores, and risk-mitigation tools like intelligent code reviews. You will lead high-impact initiatives including onboarding frontier models, scaling massive compute, and optimizing proxy systems for security and logging. The position requires expertise in Python, Docker, Kubernetes, MLOps, and zero-trust security models. You will manage multi-cloud and frontier-model integrations while establishing standards for production platforms. The core work involves solving the technical challenge of creating secure, scalable gateways that allow engineers to perform advanced data analysis and deploy trustworthy AI capabilities at a planetary scale.
What does a Software Engineer earn in Washington?
Median $202800 from 409 postings across 38 companies.
Define and drive the long-term technical vision and architecture for the AI platform and security infrastructure.
Lead high-impact initiatives with full ownership over strategy, design, implementation, and company-wide rollout.
Oversee the onboarding of frontier models and the scaling of massive compute resources.
Optimize proxy systems for security, efficiency, logging, and control.
Invent and scale advanced risk-mitigation capabilities like intelligent code review and automated PR tools.
Establish best practices for lean, secure, and effective applied AI across the entire organization.
Convert engineering know-how into production software, trained models, and automated systems at scale.
What we're looking for
Bachelor’s degree in computer science, computer engineering, or another engineering discipline and 7+ years of professional experience building and scaling production platforms.
9+ years of professional experience building and scaling production platforms in lieu of a degree.
Experience designing and operating large-scale AI/ML platforms, internal developer platforms, or mission-critical infrastructure.
Applicant must be a U.S. citizen, national, lawful permanent resident, refugee, asylee, or eligible to obtain required authorizations from the U.S. Department of State.
Demonstrated success architecting and scaling AI gateways, secure proxy layers, or enterprise MLOps platforms that serve hundreds of users (preferred).
Expert-level experience with Docker/Kubernetes ecosystems, zero-trust security models, and multi-cloud/frontier-model integrations (preferred).
Deep expertise in MLOps, distributed compute, model lifecycle management, and building highly observable, reliable systems (preferred).
Mastery of Python and broad expertise across modern languages and tooling (preferred).
Python
MLOps
Docker
Kubernetes
infrastructure-as-code
Distributed Compute
AI
ML
Zero-Trust Security
Observability
Proxy Systems
Model Lifecycle Management
Python
MLOps
Docker
Kubernetes
infrastructure-as-code
Distributed Compute
AI
ML
Zero-Trust Security
Observability
Proxy Systems
Model Lifecycle Management
Automation
AI
MLOps
Python
Docker
Kubernetes
infrastructure-as-code
Distributed Compute
Multi-cloud
Zero-trust Security
Observability
Model Lifecycle Management
Proxy Systems
Data Analysis
Automation
AI
MLOps
Python
Docker
Kubernetes
infrastructure-as-code
Distributed Compute
Multi-cloud
Zero-trust Security
Observability
Model Lifecycle Management
Proxy Systems
Data Analysis
Automation