Junior Application Security Analyst

Job Description
Junior Application security analyst - Hybrid (3 days in office) Job, 1+ Year Experience

Annual Income: $54K - $79K

A valid work permit is necessary in the US

About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.

Tasks

Tasks within the SDLC process: analysis of analyzer results, rule refinement, evaluation of their effectiveness;
Source code analysis of applications in Java;
Conducting internal penetration tests;
Diving into the workings of containerized applications, and understanding their implementations;
Preparing recommendations for identified vulnerabilities, taking into account the specifics of the technological stack and implementations.
Requirements Experience with DAST (AFL, Fuzzing, Burp), including creating custom "farms";
Experience in conducting pen tests for virtualized infrastructure;
Experience in analyzing mobile applications;
Programming skills in Java;
Experience with k8s, Docker;
Experience with traffic analyzers (Wires-listen, etc.);
Experience in automating routine security processes;
Understanding of modern software development processes and practices: Agile, SDLC, DevOps, CI/CD;
Competent written and verbal communication skills (English B1, Russian B2+).
Successful participation in Bug Bounty programs;
CTF experience;
Familiarity with OWASP Testing Guide, OWASP Code Review Guide, and OWASP Secure Coding Practices;
Experience working with and supporting HashiCorp Vault;
Experience with network vulnerability scanners (Sureness, XSpider, MaxPatrol, etc.).

Why Patterned Learning LLC?

Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate development.

The pattern recognition is particularly relevant in the context of coding. Neural networks, intense learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.