Member of Technical Staff, Application Security

Listen Labs; New York (NY); Presencial; USD 180.000 - 300.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical benefits

Learning stipend

Wellness stipend

AI tools budget

In-office meals

Descripción de la vacante

Listen Labs in New York, NY, is seeking an engineer to embed security in a complex AI-native product. You will write real code, ship fixes, and understand how systems are built enough to know where they break.

You will secure data, design multi-tenant controls, and model threats around LLM-based workflows, authentication, and API surfaces, all while shipping secure-by-default libraries and fast feedback in CI pipelines.

Formación

• Experience securing AI-native products and data
• Strong understanding of authentication/authorization and secrets management
• Ability to design secure-by-default libraries and patterns

Responsabilidades

• Design protections for data (encryption, access boundaries, retention)
• Ensure multi-tenant isolation across roles and studies
• Threat-model prompt injection and data exfiltration risks in AI systems
• Secure the Human API integrations for programmatic callers
• Build secure engineering tooling (SAST/DAST/SCA, secrets scanning)
• Review architecture and pull requests, ship fixes end-to-end

Conocimientos

Backend security engineering

TypeScript

Terraform

Threat modeling

OWASP Top 10

Security architecture

Data security

Herramientas

Terraform

CI/CD security tooling

Cloud platforms

Descripción del empleo

TL;DR: We're hiring an engineer who builds security into a complex AI-native product, not one who audits it from the outside. You write real code, ship real fixes, and understand how systems are built well enough to know exactly how they break.

BACKGROUND

As AI gets better at building things, the bottleneck shifts to knowing what to build. We're the bridge between AI systems and what humans actually want. Today our customers are companies. Soon, AIs themselves will be our customers. Our platform runs AI-moderated video interviews at massive scale. We find the right people from a network of millions, our AI conducts open-ended conversations with thousands of them in parallel, and we surface what to build next. What used to take research teams weeks per study, we do in hours. Where it's going: every interview feeds a human preference model, exposed to the world through the Human API. AI agents will call Listen from Slack, Linear, IDEs, and coding agents to ask what humans actually want. The trust people place in that signal is everything, and security is how we earn it.

Company Highlights

• World-Class Team: Founded by serial entrepreneurs, Alfred (Bemlo, YC W22) and Florian (IOI medalist, ICPC World Finalist), and built with talent from Google, Meta, Tesla, Amazon, Jane Street, and X, alongside Bain, Goldman Sachs, McKinsey, and Sequoia-backed startups like Applied Intuition, Scale AI, and Rippling. Over 30% of our team are former founders.
• Hypergrowth: Since launching, Listen has grown annualized revenue 15x and completed over 2 million interviews, backed by $100M in total funding led by Ribbit Capital, with Sequoia, Conviction, and Pear VC participating.
• Traction: Already serving 15% of Fortune 100s, with enterprise wins and expansions across Anthropic, Microsoft, Nestlé, Perplexity, Sweetgreen, and more.

What You'll Work On

• Securing the Database of Humanity. We hold voice, face, and device-linked profiles for millions of people. You'll help design the protections that data deserves: encryption, access boundaries, retention, and abuse resistance, built into the product rather than bolted on.
• Multi-Tenant Isolation. ListenLabs staff, customer admins, and study participants all touch the platform with very different privileges. You'll make sure the boundaries between tenants, roles, and studies hold up, and you'll find the places where they don't before anyone else does.
• AI-Native Attack Surface. Our interviewer, Study Composer, and Research Agent all process untrusted human input and take actions. You'll threat-model prompt injection, data exfiltration through model outputs, tool and agent permissions, and the new failure modes that appear when LLMs sit inside the trust boundary.
• Securing the Human API. As we embed Listen into Slack, Linear, IDEs, and coding agents, we're building authentication, authorization, and API surfaces for programmatic and agentic callers. You'll help get those designs right from the first version.
• Security Engineering Platform. You'll build the paved road: secure-by-default libraries and patterns, SAST/DAST/SCA and secrets scanning wired into CI, dependency and supply chain hygiene, and fast feedback so engineers can ship quickly without shipping vulnerabilities.
• Design and Code Review. You'll review architecture and pull requests alongside the engineers who wrote them, and you'll often write the fix yourself.

Who You Are

• You're an engineer first. You're comfortable in a modern backend stack (we use TypeScript and Terraform), you can read and write production code, and you can hold your own in a code review with product engineers. Junior-level or better development skills are the floor, and the engineering mindset matters more than any single language.
• You understand how things are built, and how they break. You know the OWASP Top 10 as failure patterns of real design decisions, not a checklist. You can explain why an authorization bug happens, not just that it does. You think in trust boundaries, threat models, and blast radius.
• You build the fix, not just the finding. You're not a security analyst writing tickets, and you're not a pen tester delivering a report. You close the loop by shipping the guardrail, the library, the pipeline check, or the patch.
• You have real depth in application security fundamentals: authentication and authorization (OAuth/OIDC, sessions, RBAC), cloud and infrastructure security basics, secrets management, secure API design, and common vulnerability classes across web and backend systems.
• You're curious about AI security. You're excited to learn how LLM systems fail and to help define best practices in a field that's still being written. Prior LLM security experience is a plus, not a requirement.
• You care about getting things right. Moving fast is essential, but a 100% solution is much more powerful than an 80% one. When something breaks, you go to root cause.
• You communicate complex ideas in writing. We work independently with one meeting a week, so clear writing is how you'll make the case for tradeoffs, explain risk to engineers, and get fixes prioritized.
• You solve problems end to end. You scope your own work, think about customers and their trust, and own your decisions.

LIFE AT LISTEN LABS

• Health, covered: Full medical, dental, and vision. FSA/HSA and life insurance are available too.
• Competitive Compensation: Backed by world-class investors, we hire the best and treat them accordingly, with meaningful equity ownership. The range for this role is $180,000 – $300,000 base. Actual compensation is influenced by a wide array of factors, including but not limited to skill set, experience, and work location. If this range doesn't match your expectations, we still encourage you to apply.
• Annual Learning Stipend: Books, courses, conferences, coaching, language lessons, and almost anything that makes you sharper.
• Annual Wellness Stipend: Gym memberships, race fees, massages, ergonomic gear, and more.
• Monthly Stipend for AI tools: A dedicated budget for the software and GPU credits that make you faster.
• Fed, daily: A private chef serves lunch and dinner in our SF office; NYC and London teams get daily meal credit.
• Flexible time off: A take-what-you-need vacation policy.
• We celebrate together: Company offsites and events like our annual holiday party.
• Room to grow: As an early member of the team, you'll own end-to-end processes from scratch and grow alongside the company.