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About Lantern
Lantern is the specialty care platform connecting people with the best care when they need it most. By curating a Network of Excellence comprised of the nation's top specialists for surgery, cancer care, infusions and more, Lantern delivers excellent care with significant cost savings to employers and their workforces. Lantern also pairs members with a dedicated care team, including Care Advocates and nurses, for the entirety of their care journey, helping them get back to good health, back to their families and back to work. With convenient access to specialists nationwide, Lantern means quality care is within driving distance for most. Lantern is trusted by the nation's largest employers to deliver care to more than 6 million members across the country. Learn more about us at lanterncare.com.
Lantern is the specialty care platform, connecting people with high-quality, affordable specialty care close to home. We operate in a regulated healthcare environment (HIPAA, HITRUST, SOC 2), we handle protected health information at scale, and we are becoming an AI healthcare company, with AI adoption a top company priority.
The Director, Application & AI Security owns application and AI security end to end: the security of the software and the AI systems Lantern builds. It is the seat most directly tied to our AI strategy. The job is to make it safe to move fast, building the golden paths and secure defaults that let teams ship product and adopt AI without waiting in a permission queue, while keeping PHI and external-model data egress genuinely protected.
You will lead a growing function. At hire, the team includes a senior application security engineer, with several open roles to fill across security architecture, AI/ML security, DevSecOps, and product security. You will build that team and set the secure-design standards the whole engineering organization builds against.
Our security philosophy is open by default, secure by design. Security exists to help the business move fast, safely, and the default answer is "yes, safely," because guardrails are built into the platform, pipelines, and tooling rather than enforced by someone saying no. Gates exist only where risk genuinely warrants them, and even then they are automated, fast, and transparent.
Location: Hybrid - at least 3 days/wk in our NYC, NY offices
Responsibilities:
- Own application security across the development lifecycle, covering static, dynamic, and interactive analysis (SAST/DAST/SCA/IAST), code and dependency security, API security, and runtime protections such as WAF and API gateways, all delivered through engineering teams rather than filed as findings.
- Own AI and ML security, including model and agent security, prompt-injection and tool-use risk, and data-egress controls for third-party model providers, plus continuous AI security posture management informed by the OWASP LLM Top 10, MITRE ATLAS, and AI risk-management standards (NIST AI RMF, ISO/IEC 42001).
- Own security architecture and threat modeling, including secure-by-design review and ownership of cryptographic standards.
- Own DevSecOps and pipeline security, with scanning as a default in CI/CD and MLOps/LLMOps pipelines, release gating calibrated to risk, and software supply chain and SBOM practice.
- Own the security-review intake as a single front door with risk-based triage, reported against a turnaround commitment, so security accelerates the business rather than bottlenecking it.
- Own the application and AI security tool stack and the secure-design standards behind it.
Key Deliverables in Your First Year:
- An AI golden path: an approved-model catalog and automated data-egress controls, so teams adopt AI on a governed route instead of case-by-case approvals.
- A secure SDLC paved road, with scanning in the pipeline by default and critical findings resolved within SLA.
- A single security-review intake that delivers fast, risk-based answers on a published turnaround.
- A built-out team, with the open roles filled and performing.
How You Will Work:
Much of this scope is delivered in partnership. Engineering carries remediation on application, API, and dependency findings. Platform Engineering operates the CI/CD pipelines you secure. AI Engineering builds the model integrations you govern on the security side, while the Governance, Risk & Compliance team owns AI use governance, meaning acceptable use, model inventory, and third-party model data-egress scoping, as the companion to your security accountability. Setting and holding clear service expectations across these partners is part of the role.
Requirements:
- A minimum of 8 years application or product security.
- A minimum of 3 years leading a team with function-level accountability.
- Demonstrated AI and ML security ownership at production scale, including model and agent security, prompt-injection and tool-use risk, and data-egress control for third-party model providers, with working fluency in the OWASP LLM Top 10, MITRE ATLAS, and AI risk-management standards (NIST AI RMF, ISO/IEC 42001).
- Secure SDLC leadership across static, dynamic, and interactive analysis (SAST/DAST/SCA/IAST), dependency and software supply-chain risk, SBOM practice, and remediation delivered through engineering teams.
- Security architecture and threat-modeling depth, with the standing to review a design and be heard by senior engineers.
- DevSecOps and pipeline security experience, including scanning as a default in CI/CD, risk-calibrated release gating, and pipeline secret handling.
- API security experience on a platform handling regulated data.
- A track record of delivering security as paved roads and secure defaults rather than review gates, with evidence of adoption.
- Bachelor's degree in a relevant field, or equivalent professional experience.
Strong Candidates Will:
- Healthcare or another regulated domain where PHI or comparable data flows through AI systems.
- Experience standing up an AI golden path, including an approved-model catalog, a governed adoption route, and automated egress controls.
- Experience building a single security-review intake with risk-based triage and a turnaround commitment.
- Cryptographic standards ownership, including customer-managed key models.
- Hands-on familiarity with tools such as Snyk, GitHub Advanced Security, AI security posture management (for example, Wiz), and AI-assisted code scanning.
- Familiarity with AI red-teaming and adversarial-testing tooling (for example, Garak, PyRIT, Promptfoo).
- Product security experience shipping customer-facing security features as differentiators.
- Experience hiring and building a team from a single senior individual contributor.
- CSSLP, OSWE, GWAPT, or equivalent.
Who Thrives in This Role:
- Guardrails over gates. You instinctively ask how to remove a queue rather than how to staff one.
- Engineering credibility. You earn influence with builders through technical depth, not process authority.
- Risk calibration. You reserve genuine gates for what earns them, namely PHI exposure, external model egress, and privileged access, and you keep even those automated and fast.
- Delivery orientation. You treat a roadmap as a commitment with dates.
- Team building. You can grow a function from one senior IC and a set of open roles.
Benefits
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Short & Long Term Disability
- Life Insurance
- 401k with company match
- Flexible Time Off
- Paid Parental Leave
The salary range for this position is $200,000 - $275,000 annually, based on experience, skills, and qualifications. This position is also eligible for an annual bonus, separate from and in addition to the base salary range listed above. Lantern provides this range in good faith in compliance with New York's pay transparency requirements.
About You:
- You use LOGIC in your decision making and understand that progress is critical to making change. You focus on the execution of your content while balancing a fast-paced environment and you take the time to celebrate both the small & big wins.
- INCLUSION is a core tenant of your personal beliefs. A diverse and inclusive environment is incredibly important to you. You understand and desire to be a part of a diverse team with different experiences and perspectives & you cherish the differences in each individual that you interact with.
- You have the GRIT, drive and ambition to tackle big problems. Big problems require big ideas and a team that supports new ideas.
- You care deeply for your customers are driven to keep HUMANITY in all decisions. Your customers aren't just the individuals using your product. They are the driving factor in your motivation to make a change.
- Integrity guides you in life. Focusing on the TRUTH vs. giving people the answers they want to hear.
- You thrive in a Team Environment. Collaboration is key in innovation and creating change.
These pillars of LIGHT are a reminder to our team that we are making a difference by providing guidance and support in navigating the often complex and confusing landscape of healthcare. We hope that through this LIGHT, individuals can find their way to the best care, resources, and support they need to get back to life.
If this sounds like you, we would love to connect to speak further about career opportunities at Lantern.
Please apply to our role & someone from our Talent Acquisition Team will reach out to help you navigate our interview process.
Lantern does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.
Salary Context
This $200K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Lantern, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($237K) sits 12% above the category median. Disclosed range: $200K to $275K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Lantern AI Hiring
Lantern has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Dallas, TX, US, Dallas, TX, New York, NY, US. Compensation range: $275K - $275K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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