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The Senior AI Enablement \+ Adoption Analyst will serve as a bridge between strategy and execution, playing a critical role in driving the successful deployment, configuration, training, support and adoption of artificial intelligence solutions across the Firm. This role will focus primarily on generative AI platforms and applications, while also supporting other AI capabilities, including search, document classification, term and fact extraction and advanced analytics.
The position will work closely with lawyers, practice groups, knowledge professionals, IT, the Office of the General Counsel, Information Governance and other business teams to ensure AI tools are promoted, configured and adopted in practical, effective and responsible ways. This role will require a mix of technical fluency, business focus, customer service, project management and the ability to map platform capabilities to legal and business needs.
The Senior AI Enablement \+ Adoption Analyst will independently lead defined workstreams, support cross\-functional implementation efforts, develop training and adoption approaches and help translate user feedback into practical improvements to the Firm’s AI solutions. In particular, the role will proactively engage with frequent and advanced users to understand what is working, what is not and where opportunities exist to improve the user experience, inform internal development priorities and shape practical asks of external vendors.
Essential Job Duties \& Responsibilities
- Lead and own complex AI enablement and adoption workstreams from concept and pilot stages into practical, scalable solutions for lawyers and business professionals across the Firm.
- Drive the execution of the Firm’s AI outreach, engagement and adoption plans in coordination with Knowledge \& Innovation, Legal Talent, IT, the Office of the General Counsel, Information Governance, Client Development and Engagement and other relevant functions.
- Develop and maintain deep subject matter expertise of the Firm’s AI platforms, related workflows and user needs, serving as a go\-to resource and trusted authority on tool capabilities, configurations and best practices.
- Conduct outreach to active and advanced users of the Firm’s AI tools to understand what is working well, where friction points remain and what enhancements may improve user experience and adoption.
- Gather, clarify and prioritize user feedback, usage trends and emerging AI use cases, and translate those insights into practical recommendations for platform configuration, internal development priorities, training improvements and vendor discussions.
- Lead the review and evaluation of new AI applications, features and service offerings for functional viability and fit with Firm needs.
- Direct and coordinate with project managers, data scientists, software developers, data engineers, knowledge management lawyers, practice solutions professionals and other teams to move concepts into workable solutions.
- Develop and maintain user\-facing training materials, reference guides and best practices for Firm AI tools, as well as internal documentation and processes to support consistent knowledge sharing and operational efficiency across the team
- Deliver trainings, walkthroughs, office hours and other enablement sessions on the effective and responsible use of Firm AI solutions, tailoring content and approach to diverse audience levels.
- Provide ongoing functional support to users, respond to requests for assistance and help resolve more complex issues through coordination with internal teams and external vendors, as appropriate.
- Oversee pilot programs, phased rollouts and post\-launch adoption efforts by collecting feedback, identifying issues and recommending improvements.
- Develop and refine communication, onboarding and change\-management approaches to encourage adoption and sustained use across different user groups.
- Help determine, track and analyze key performance indicators and other metrics relating to platform adoption, engagement, user satisfaction and impact, and report insights and recommendations to leadership.
- Partner with external vendors to track, evaluate and manage product feature releases, coordinate internal testing and readiness efforts and ensure new capabilities are effectively communicated and deployed across the Firm.
- Work closely with the Office of the General Counsel, Information Governance and other relevant stakeholders to support compliant, ethical and responsible use of AI tools in accordance with Firm guidance.
- Stay current on developments in generative AI, natural language processing, information retrieval and related legal technology markets, and identify trends and opportunities relevant to the Firm.
- Handle projects on request under the direction of the Chief Knowledge and Innovation Officer, the Director of Applied Analytics \+ AI, the Associate Director of AI Enablement \+ Adoption and other executive staff.
Education
- A bachelor’s degree is required, preferably in data science, mathematics, statistics, computer science, engineering, finance or a related field or equivalent working experience.
- Prior coursework or certifications in artificial intelligence, natural language processing, information retrieval, product ownership, project management (PMP) or change management are a significant plus.
Skills and Experience
- 7\+ years of overall professional experience required.
- 3\+ years of experience with artificial intelligence, legal technology, knowledge management, information retrieval, document management, practice solutions, change management, and product support.
- Experience supporting, implementing, evaluating or driving adoption of AI\-enabled or technology\-enabled solutions in a professional services environment strongly preferred.
- Experience in the legal industry is strongly preferred, along with an understanding of the processes, terminology and challenges faced by lawyers and legal support professionals.
- Strong working knowledge in artificial intelligence and its applications in the legal industry, particularly generative AI and related legal technology tools.
- Strong project management and organizational skills, with the ability to manage multiple workstreams, prioritize effectively and adapt quickly to shifting priorities.
- Demonstrated ability to gather, synthesize and translate user feedback, business needs and usage trends into practical recommendations, support resources and solution improvements.
- Strong stakeholder\-management skills and the ability to work effectively with lawyers, business professionals, technical teams and external vendors.
- Strong analytical and problem\-solving skills, with the ability to assess platform capabilities, identify friction points and recommend practical solutions.
- Excellent written, verbal and presentation skills, including the ability to communicate technical concepts and product limitations clearly to non\-technical audiences.
- Experience developing and delivering training, reference materials, best practices and other enablement resources for end users.
- Experience coordinating with external technology vendors on product roadmaps, feature releases and issue resolution.
- Proven ability to work independently and as part of a team, with a proactive, collaborative, adaptable and solution\-oriented approach.
- Sound judgment and familiarity with data privacy, information governance, confidentiality, ethics and responsible\-use considerations relevant to AI tools in a law firm environment.
- Experience mentoring, coaching or guiding junior analysts or team members, including providing constructive feedback, supporting professional development and fostering a collaborative team environment.
- Comfortable working closely with both technical resources and lawyers and quickly switching between those groups.
- Proactively develops and maintains functional knowledge in emerging AI, natural language processing and legal technology areas.
Salary Information
NY Only: The estimated base salary range for this position is $140,000 to $165,000 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.
\#LI\-Hybrid
Salary Context
This $140K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($152K) sits 30% below the category median. Disclosed range: $140K to $165K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Simpson Thacher & Bartlett LLP AI Hiring
Simpson Thacher & Bartlett LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $165K - $165K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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