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About This Role
The Senior Security Engineer, AI Security is a hands-on technical expert responsible for designing, implementing, and continuously enhancing the firm's AI Security Program. This role will serve as a key security partner to Application Security, Product Development, Technology Architecture, Infrastructure, and AI Enablement teams to ensure AI technologies are deployed securely, responsibly, and in alignment with technologies are deployed securely, responsibly, and in alignment with business, regulatory, and risk management requirements.
This role is responsible for implementing security controls across the AI ecosystem, including large language models (LLMs), AI coding assistants, Agents, AI gateways, and platforms such as Claude. The successful candidate will establish security guardrails, monitor AI risks, implement technical controls, and support governance processes that enable secure AI adoption.
The ideal candidate brings deep expertise in AI, application, and cloud security, with the ability to navigate complexity, solve complex problems, work effectively amid ambiguity, and deliver measurable security outcomes.
ESSENTIAL JOB DUTIES & RESPONSIBILITIES
- Lead the implementation, maturation, and continuous improvement of the enterprise AI Security Program.
- Develop and operationalize AI security standards, control frameworks, secure design patterns, implementation baselines, and governance processes that support the secure evaluation, onboarding, deployment, and monitoring of AI technologies.
- Serve as the lead security engineer for enterprise AI platforms, including Claude, AI coding assistants, AI gateways, Agents, MCP-based integrations, and other emerging AI technologies.
- Design, implement, and continuously improve security controls including identity and access management, authorization models, tenant security configurations, encryption, logging, monitoring, and data protection safeguards.
- Develop and operationalize AI guardrails to mitigate risks including prompt injection, jailbreaks, unauthorized access, excessive agency, sensitive data exposure, model misuse, and adversarial attacks.
- Develop security automation, governance automation, and policy-as-code capabilities that enable scalable AI security operations.
- Contribute to security architecture reviews and threat modeling activities for AI-enabled applications, platforms, agents, workflows, and integrations.
- Establish secure development standards for AI-enabled software development practices.
- Support security operations teams in monitoring and responding to AI security threats and emerging adversarial techniques.
- Partner with Data Protection, Privacy, Compliance, and Governance teams to implement safeguards for the secure use of data within AI platforms.
- Function as the firm's subject matter expert on AI security, emerging threats, adversarial AI risks, and industry best practices.
- Provide technical security engineering leadership for AI security innovation, proof-of-concepts, and future-state capabilities that improve security and business enablement.
- Define, and report key performance indicators (KPIs) to measure security effectiveness and maturity.
- Stay current on emerging threats, technologies, and industry trends to proactively reduce organizational risk.
- Build strong cross-functional partnerships across global teams and external stakeholders.
EDUCATION
- Bachelor’s degree in information security, IT, related discipline, or equivalent experience
- Professional certifications such as CISSP, CCSP, CISM, or similar preferred
SKILLS AND EXPERIENCE
- 10+ years of experience in security engineering, application security, cloud security, or a related cybersecurity discipline, with 3+ years of hands-on experience securing AI platforms, AI-enabled applications, or enterprise AI solutions.
- Deep knowledge of AI security principles and frameworks, including OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework (AI RMF), MITRE ATLAS, secure AI development practices, and AI governance principles.
- Strong understanding of AI threats including prompt injection, jailbreaks, data poisoning, model manipulation, excessive agency, MCP vulnerabilities and sensitive data exposure.
- Experience securing enterprise AI platforms such as Claude, Microsoft Copilot, AI coding assistants, AI agents, or similar technologies.
- Experience implementing identity, authentication, authorization, RBAC, API security, and privileged access controls for AI platforms.
- Experience building security automation, policy-as-code, and governance automation to support AI security operations at scale.
- Knowledge of Secure SDLC, OWASP Top 10, API Security, software supply chain security, container security, and DevSecOps best practices.
- Demonstrated ability to build developer-friendly security controls that balance risk management with engineering productivity.
- Ability to manage multiple concurrent objectives and activities and make effective judgments in prioritizing and time allocation.
- Must be able to build collaborative relationships and is comfortable interacting frequently with leadership and internal/external stakeholders.
Salary Information
NY Only: The estimated base salary range for this position is $180,000 to $215,000 at the time of posting.
The actual salary offered will depend on a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location in which the applicant lives and/or from which they will be performing the job. This role is exempt meaning it is not overtime pay eligible.
Simpson Thacher will not sponsor applicants for work visas for this position.
Privacy Notice
For information about how Simpson Thacher & Bartlett LLP collects and processes your personal information, please refer to our Privacy Notice available at https://www.stblaw.com/other/privacy-notice .
Simpson Thacher & Bartlett is committed to a collegial work environment in which all individuals are treated with respect and dignity. The Firm prohibits discrimination or harassment based upon race, color, religion, gender, gender identity or expression, age, national origin, citizenship status, disability, marital or partnership status, sexual orientation, veteran’s status or any other legally protected status. This Policy pertains to every aspect of an individual’s relationship with the Firm, including but not limited to recruitment, hiring, compensation, benefits, training and development, promotion, transfer, discipline, termination, and all other privileges, terms and conditions of employment.
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Salary Context
This $180K-$215K range is above the median 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 Simpson Thacher & Bartlett LLP, 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 Required
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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($197K) sits 7% below the category median. Disclosed range: $180K to $215K.
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.
Simpson Thacher & Bartlett LLP AI Hiring
Simpson Thacher & Bartlett LLP has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $215K - $220K.
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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