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About This Role
Job Summary & Objectives
The AI Platform Manager will serve as the primary business owner and strategic lead for assigned enterprise GenAI platforms (e.g., Claude, Harvey). This role is responsible for the product roadmap and strategy, vendor relationship, governance and risk coordination, platform administration, feature evaluation and release management, license management and utilization, business value realization, and ROI.
The position will work closely with lawyers, practice groups, Knowledge Management, Information Technology, Information Governance, the Office of the General Counsel, Client Development and Engagement and other business functions to ensure AI platforms are deployed responsibly, securely and effectively. The role serves as the bridge between business needs and technology capabilities, translating legal and business professional use cases into practical workflows, scalable solutions and product priorities.
AI platforms are evolving rapidly, with vendors regularly introducing new capabilities, integrations, governance controls, agents, workflows and security-related features. The AI Platform Manager will continuously evaluate these developments and provide dedicated ownership across product strategy, vendor management, roadmap planning, feature evaluation, governance coordination, release readiness, user engagement and adoption.
The role will independently lead product initiatives and cross-functional workstreams while coordinating the governance, risk, security, compliance, client confidentiality, training, communications and operational activities required to successfully deploy and scale AI solutions across the Firm. Strategic recommendations and key decisions will be escalated to the Associate Director of AI Enablement + Adoption and other Firm leadership, as appropriate.
Essential Job Duties & Responsibilities
- Serve as the primary product owner and business lead for assigned enterprise AI platforms, responsible for strategy, roadmap development, release management, governance, adoption and overall platform success.
- Maintain deep subject matter expertise in assigned platforms, configurations, integrations, workflows and best practices, while continuously assessing new capabilities, agents, governance controls and security features for business value, strategically fit and operational readiness.
- Establish and lead a structured release-management process for evaluating, testing and deploying new capabilities, while coordinating the governance, operational, training and adoption activities required to support frequent vendor releases and platform updates.
- Partner with Information Technology, Information Security, Information Governance, the Office of the General Counsel and other stakeholders to evaluate risk, security, compliance and confidentiality considerations, and develop governance frameworks, guardrails and deployment standards that support responsible AI use.
- Manage strategic vendor relationships, including roadmap discussions, service reviews, issue escalation, feature requests, renewal planning and advocacy for Firm priorities.
- Coordinate with project managers, data scientists, software developers, data engineers, knowledge management lawyers, practice solutions professionals and other teams to translate product concepts into scalable solutions.
- Establish structured feedback mechanisms and engage directly with lawyers, business professionals and advanced users to identify opportunities, prioritize enhancements and improve overall platform value and user experience.
- Collaborate with AI Enablement + Adoption leadership and peer stakeholders to promote consistent product management practices, governance standards and adoption approaches across the Firm's AI ecosystem.
- Drive the Firm’s AI enablement and adoption strategy for assigned platforms through targeted communications, onboarding, training, office hours, practical guidance and change-management initiatives.
- Partner with lawyers, practice groups and business professionals to identify high-value use cases and translate them into practical AI-enabled workflows and solutions.
- Establish, track and analyze key performance indicators related to adoption, engagement, utilization, user satisfaction, business impact, platform value and return on investment.
- Prepare concise executive-level updates and recommendations regarding product performance, roadmap decisions, vendor developments, governance considerations, adoption trends and investment priorities.
- Monitor developments in generative AI, natural language processing, information retrieval and the legal technology market to identify relevant risks, trends and opportunities for the Firm.
- Provide functional escalation support for complex product questions and issues, coordinating resolution with internal technical teams and external vendors as appropriate.
- Serve as the Firm's designated business owner for assigned AI platforms, ensuring product strategy, governance, vendor roadmaps, user needs and Firm priorities remain aligned as technologies evolve.
- 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 business, computer science, information systems, data science, engineering, finance or a related field.
- Prior coursework or certifications in product management, product ownership, artificial intelligence, project management (PMP), change management, information security or related disciplines are a significant plus.
Skills and Experience
- 7+ years of overall professional experience required.
- 5+ years of experience in product management, product ownership, legal technology, knowledge management, artificial intelligence, enterprise software, digital transformation, or related disciplines.
- Experience serving as a product manager, product owner, platform owner or business lead for enterprise technology platforms, including responsibility for product strategy, roadmap development, release management, vendor coordination and cross-functional delivery.
- Strong working knowledge of generative AI and its application within the legal industry. Direct experience implementing, administering, governing or driving adoption of Claude, Harvey or similar legal AI platforms is strongly preferred.
- Experience in the legal industry is strongly preferred, along with an understanding of the workflows, terminology, confidentiality obligations and challenges faced by lawyers and legal support professionals.
- Experience evaluating, implementing and governing AI-enabled technologies within a regulated or professional services environment, including considerations related to privacy, confidentiality, information security, compliance, ethics and responsible AI usage.
- Experience managing external technology vendors, including roadmap planning, feature evaluation, service reviews, issue escalation and renewal support.
- Strong project and program management skills, with the ability to manage multiple products and workstreams, prioritize effectively and adapt quickly to shifting priorities.
- Strong analytical, product-thinking and problem-solving skills, with the ability to synthesize user feedback, business requirements, usage data and technology trends into practical product decisions and recommendations.
- Strong stakeholder management, communication and change-management skills, with experience building alignment across lawyers, business professionals, technical teams, Firm leadership and external vendors while driving product adoption and user engagement.
- Excellent written, verbal and presentation skills, including the ability to communicate technical concepts, product limitations, risk considerations and recommendations clearly to non-technical and executive audiences.
- Experience defining and interpreting product and adoption metrics, preparing executive reporting and using data to guide product priorities and investment decisions.
- Proven ability to work independently and as part of a team, with a proactive, collaborative, adaptable and solution-oriented approach.
- Experience mentoring, coaching or guiding analysts or other team members, including providing constructive feedback and supporting professional development.
- Comfortable working closely with both technical resources and lawyers and quickly switching between strategic, operational and user-facing responsibilities.
- Demonstrated curiosity and ability to quickly develop expertise in emerging AI technologies, vendor ecosystems and evolving legal technology trends.
Salary Information
NY Only: The estimated base salary range for this position is $190,000 to $220,000 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 $190K-$220K 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. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $190K to $220K.
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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