Senior Application Security Engineer, Spring Health

• 7+ years of professional experience in application security or a closely related security engineering discipline, including experience working on complex, ambiguous problem areas independently
• Hands-on experience with DAST, SAST, and SCA tools, and manual testing techniques (OWASP, SANS Top 25)
• Demonstrated experience securing CI/CD pipelines with commercial and custom-built tooling
• Experience with IaaS cloud infrastructure (AWS, Azure, or GCP), container technologies, and service-oriented architectures
• Security automation experience in at least one of: Go, Python, JavaScript, or Ruby
• Familiarity with AI/ML security concepts — prompt injection, adversarial inputs, model supply-chain risks, and the OWASP LLM Top 10
• Working knowledge of AI and LLM tooling (e.g., OpenAI, Anthropic, LangChain, or equivalent) sufficient to assess security risk and integrate into automated workflows
• Experience implementing controls aligned to NIST CSF, HIPAA, HITRUST, ISO-27001, or SOC-2
• Strong cross-functional collaboration skills, with experience working alongside engineering, product, and leadership stakeholders to define and advance security priorities and plans
• Bachelor’s degree in Computer Science, Engineering, MIS, IT, or equivalent work experience
• Research shows that women and people from historically underrepresented communities often hesitate to apply for roles unless they meet every qualification compared to other similarly-qualified candidates. At Spring Health, we are committed to fostering a workplace where everyone feels valued, empowered, and supported to Thrive. If this role excites you, we encourage you to apply

Desirable

• Spring Health is looking for a Senior Application Security Engineer II to join our growing Application Security team
• Reporting to the Manager, Application Security, you will play a key role in maturing and expanding our AppSec programs — including established SAST, SCA, and DAST capabilities — while helping shape new initiatives such as a Secure AI Development Lifecycle (ADLC)
• You will work alongside a team of engineers who have laid a strong foundation, bringing your experience to help take these programs to the next level
• Contribute to the advancement of secure-by-design practices within the team’s S-SDLC program, including participation in architecture reviews, design consultations, and security guidance across the development lifecycle
• Mentor engineers on secure coding practices, AppSec fundamentals, and career growth, fostering a collaborative environment where the team grows stronger together
• Facilitate the development of an AI-assisted threat modeling program, spanning risk identification, security architecture, and proactive program maturity, enabling the ability to scale threat modeling across the organization
• Contribute to maturing the team’s established SAST, SCA, and DAST programs through rule tuning, coverage improvements, and identifying opportunities to strengthen security controls as the organization scales
• Perform security-focused code reviews of internal and open-source libraries, prioritizing findings by exploitability and business impact
• Support vulnerability remediation efforts by assessing impact, proposing solutions, and validating fixes in accordance with the team’s established remediation workflows
• Identify and implement process improvements and security automation using languages such as Go, Python, JavaScript, or Ruby, including the integration of AI tooling to improve team workflows and program efficiency
• Contribute to security assessments of AI-integrated product features, including LLM APIs, vector databases, and RAG pipelines, with a focus on risks such as prompt injection, data leakage, and model supply-chain vulnerabilities
• Contribute to the research, design, and development of a Secure AI Development Lifecycle (ADLC) in accordance with the OWASP Top 10 for LLM Applications and emerging adversarial ML guidance
• Evaluate and recommend AI-assisted security tooling, including AI-augmented SAST and LLM-powered code review, to improve program coverage and team efficiency
• What success looks like:
• Demonstrated improvements to the team’s SAST, SCA, and DAST programs through rule tuning, noise reduction, and coverage expansion within the first 90 days
• Delivery of a documented AI-assisted threat modeling program and foundational ADLC framework, including defined processes, tooling recommendations, and adoption milestones
• Consistent adherence to team SLAs for vulnerability triage and remediation, with measurable contributions to reducing time-to-remediation for high and critical findings
• Delivered security automation and AI tooling integrations that produce measurable improvements to program efficiency or engineering team experience
• Completed security assessments of AI-integrated product features with documented findings, risk ratings, and remediation guidance delivered to engineering teams