AI Software Engineer
US$90,000 – US$130,000 / yr base salary
Posted 18 Aug 2026
Advertised as “Junior AI Software Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 0–4 yrs
- Management level
- Individual contributor
| Base salary | US$90,000 – US$130,000 / yr |
|---|
Field of studyComputer Science, Engineering, Information Systems (or related)
Study levelBachelor's
CertificationsAWS Certified Cloud Practitioner
Study levelMaster's
Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We’re proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission’s biggest challenges.
Dark Wolf is seeking a Junior / Entry-Level AI Software Engineer to support the construction, testing, and deployment of artificial intelligence and machine learning pipelines in secure, high-assurance environments. In this role, you will work alongside senior technical leads to maintain data processing workflows, implement foundational software code, and assist in containerizing software components
Key Responsibilities
- Assist with building and maintaining scalable ML data ingestion pipelines.
- Support code development and scripting using Python and Bash.
- Containerize application modules using Docker for deployment.
- Perform regular unit testing and execute automated security scanning tools to ensure code health and compliance.
- Participate in daily Agile activities, code reviews, and backlog refinement sessions.
Required Qualifications:
- Clearance: Must be a US Citizen holding an active TS/SCI security clearance with an active Full-Scope Polygraph.
- Education: Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field (Master’s degree preferred).
- Experience: 0-4+ years of software engineering experience.
Technical Competencies:
- AI Frameworks: Basic familiarity with PyTorch or TensorFlow.
- AI Models: Basic understanding of model evaluation techniques and foundational prompt engineering.
- Machine Learning: Core understanding of supervised/unsupervised learning concepts and feature engineering.
- Programming & Scripting: Practical competency in Python and fundamental scripting tools.
- Containerization: Experience building and running basic Docker images.
- Agile Frameworks: Working knowledge of Agile, Scrum, or Kanban concepts.
- Multi-Cloud Architecture: Basic familiarity with cloud platforms such as AWS or Azure.
- Security Scanning Tooling: Hands-on exposure to static code analysis and security scanning tools (e.g., SonarQube).