Applied AI Engineer: Edge AI for Defense Systems

Socket.dev

San Francisco (CA)

On-site

USD 150,000 - 170,000

Full time

13 days ago

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Benefits offered by this job

Health benefits

401k match

Free daily lunch

No meeting Fridays

Unlimited PTO

Job summary

CHAOS Industries in San Francisco seeks an Applied AI Engineer to develop, integrate, and deploy AI/ML-powered capabilities across our product lines. You will collaborate with data scientists, software engineers, product teams, and mission stakeholders to turn research into reliable software for defense applications.

The role emphasizes strong data science and software engineering fundamentals, with experience applying AI/ML techniques to real products.

Qualifications

BS/MS in Computer Science, Engineering, Machine Learning, Applied Mathematics, Physics, or related field.
2+ years of professional software development experience.
Strong Python programming skills.
Experience building, testing, deploying, and supporting production software systems.
Experience with AI/ML model integration, model serving infrastructure, or model lifecycle management.
Experience with model evaluation, benchmarking, robustness testing, interpretability, or ML observability.
Familiarity with APIs, distributed systems, containers, CI/CD, observability, and edge deployment environments/GPU optimization.

Responsibilities

Build applied AI systems across CHAOS product lines, including model integration, inference services, evaluation pipelines, and production-facing AI capabilities.
Perform research and build products with product and mission teams to research, collect data, verify hypotheses and create robust, testable, maintainable models.
Evaluate and improve model performance under real-world conditions, including adversarial GPS-denied environments and low-power/edge deployments.
Develop production-quality software for data pipelines for acquisition, model serving, monitoring, lifecycle management, data processing, and system integration.
Create rapid prototypes with mission and product teams and iterate toward production-ready implementations.
Contribute to AI system reliability through testing, benchmarking, observability, interpretability, failure analysis, and performance optimization.
Learn quickly from existing codebases and domain experts to drive execution.
Manage time effectively across meetings, technical discovery, implementation, experimentation, and production support.