Senior Application Security Engineer II

Spring Health is a global mental health company building an AI-native platform to expand access to personalized care. The Senior Application Security Engineer II will mature application security programs, including SAST, SCA, DAST, threat modeling, vulnerability remediation, and a Secure AI Development Lifecycle. The role also involves mentoring engineers, conducting security reviews and assessments, and implementing security automation and AI-assisted tooling.

Responsibilities:

• 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

Requirements:

• This is a full-time, fully remote position open to candidates residing within the United States. Occasional travel to our NYC headquarters may be required
• 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
• 3+ years of demonstrated experience in security architecture, including designing and reviewing security controls across cloud-based, distributed, or service-oriented systems
• Experience leading or contributing to the development of a formal threat modeling program, including tooling selection, methodology design, and adoption across engineering teams
• Hands-on experience evaluating or implementing AI security tooling, including AI-augmented testing, LLM security assessments, or automated risk analysis
• Experience managing a bug bounty or vulnerability disclosure program
• Experience in digital health, healthcare technology, or other HIPAA-regulated environments