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About This Role
Cooley is seeking an Associate Director of Technology Collaboration & Artificial Intelligence (“AI”) Services to join the Technology Platforms and Applications team.
About Cooley: Cooley is a global law firm with an expansive practice and more than 3,000 employees and partners worldwide. We value and celebrate diverse perspectives and strive to create a workplace where every individual can thrive. At the core of Cooley’s success is the exceptional talent and unwavering spirit of our people. We embrace individuality while fostering a ‘one-firm’ culture where collaboration and creativity thrive. Our people are the foundation of our success and the driving force behind everything we do.
Hybrid Schedule: As part of the Cooley culture, we recognize and appreciate the value of being together, in person, to build comradery with others in the office and to be a contributing member of the Cooley office. However, we also appreciate the benefits and flexibility that come from remote working. As such, the default assumption for employees and partners is a hybrid schedule: some in-office presence and some work from home days absent certain essential in-office roles that require five (5) days/week in-office.
Position summary:
Cooley Technology embraces a culture of customer-service excellence, and all members of the department are expected to advance this commitment. The Associate Director of Technology Collaboration & AI Services will provide strategic and operational leadership for two enterprise service areas: Digital Workplace Collaboration and Enterprise AI.
Through two direct reports, one leading each area, the Associate Director sets strategy, establishes priorities, develops talent, and ensures coordinated delivery across the portfolio. The role is accountable for the implementation, adoption, governance, optimization, and lifecycle management of the firm’s general-purpose enterprise AI platforms and digital workplace collaboration ecosystem. This includes, but is not limited to, Microsoft 365, Outlook and Exchange Online, Teams, SharePoint Online, OneDrive, Power Platform, the firm’s intranet, enterprise search, workflow solutions, and core productivity applications. Working across Technology and the Firm, the Associate Director ensures these platforms deliver secure, reliable, and intuitive experiences; improve how work is performed; and maximize the value of the firm’s technology investments. Specific duties and responsibilities include, but are not limited to, the following:
Position responsibilities:
Strategic Leadership & Management:
- Own the enterprise AI portfolio from strategy and platform selection through configuration, governance, launch, adoption, support, and continuous improvement
- Lead M365 strategy, adoption, and optimization across Exchange Online, Teams, Power Platform, SharePoint Online, OneDrive, core productivity applications, and the firm's intranet to reduce friction and improve digital work experience
- Drive workflow automation and low-code/no-code initiatives to automate and improve
- Build and lead high-performing teams focused on user experience, change enablement, and continuous improvement
- Establish and maintain operating models, playbooks, and decision frameworks for enterprise AI, Power Platform, Teams/SharePoint provisioning, intranet content and workflow solutions
- Evaluate and guide the controlled introduction of emerging enterprise AI capabilities, e.g., agents, connectors and plugins, early-access models, and AI-enabled workflows
- Collaborate with Practice Innovation, Legal Education and Technology Learning & Adoption to ensure cohesive organizational change management planning and execution to drive adoption and build our users’ digital dexterity leveraging AI through compelling learning experiences
Product Management & Operations:
- Oversee product roadmaps, service strategy, lifecycle management, and ongoing support for enterprise AI, M365, Exchange Online, Power Platform, SharePoint, intranet, and related collaboration applications
- Maintain portfolio-level priorities, roadmaps, status reporting, playbooks, and decision records across concurrent initiatives
- Use analytics and insights (site analytics, heatmaps, engagement metrics) to measure adoption, platform health, intranet engagement, and business outcomes
- Oversee licensing, budgeting, vendor relationships, procurement coordination, contracts, and renewals for M365, enterprise AI, workflow, survey, and related collaboration tools and services.
- Ensure compliant use of platforms and AI capabilities in partnership with Information Governance, Security, Legal, and Procurement stakeholders, including review of data handling, retention, monitoring, and client-use requirements
- Set expectations for reliable, accountable service delivery, including clear ownership, effective documentation, testing and validation, transparent communication, and collaborative resolution of issues affecting attorneys, paralegals, and business professionals
Team Leadership & Development:
- Serve as direct supervisor and mentor to direct reports
- Provide day-to-day supervision of direct reports, ensure compliance with assigned work hours and monitor for compliance with all firm and department policies. Manage staffing coverage, review and process time logs/time off requests
- Support business professional development and continued educational opportunities
- In collaboration with immediate supervisor and central HR, participate in hiring, performance appraisals, counseling, termination, and other employee lifecycle events
User Engagement & Change Enablement:
- Champion a human-centered, persona-based approach to the design, configuration, delivery, and support of technology products and services
- Establish an integrated adoption and change-enablement strategy for enterprise AI and collaboration technologies
- Partner with Practice Innovation, Client Services, Learning and Development, Legal Education, and other stakeholders to coordinate organizational-change efforts
- Oversee firmwide onboarding, learning, communication, adoption, and ongoing-enablement programs, including resource centers, champions networks, communities of practice, office hours, and department-specific campaigns
- Build the firm’s digital dexterity by helping users understand how to apply AI, collaboration tools, and workflow capabilities securely and effectively
- Maintain feedback mechanisms that identify user needs, adoption barriers, service gaps, and opportunities for improvement
- Communicate strategies, decisions, progress, risks, and outcomes clearly to audiences ranging from technical teams to firm leadership
- Develop and oversee firm-wide onboarding, training, adoption, and ongoing enablement programs for AI and collaboration technologies, including resource centers, champions, communities of practice, and department-specific campaigns in partnership with Learning & Development and Legal Education
- Advocate for the user with a human-centric and persona-focused approach to tool and service configuration and delivery
- All other duties as assigned or required
Skills & experience:
Required:
- After orientation at Cooley LLP, proficiency in the use of the AI, the Microsoft Office suite, iManage and other firm applications
- Ability to work extended and/or weekend hours, as required
- Ability to travel, as required
- Bachelor’s Degree
- 8+ years of applicable experience (e.g., technology leadership, enterprise platforms, workflow automation, or legal technology product management) with 4+ years management experience (some at a senior level preferred)
- Deep knowledge of enterprise AI platforms (ChatGPT, Claude, etc.) and Microsoft 365, productivity and collaboration tools
- Experience managing external development teams and vendors
Preferred:
- Supervisory Experience
- Experience working in a law firm or other professional services organization
- Experience leading the secure, responsible deployment and adoption of enterprise AI solutions to improve business processes
- undefined
Competencies:
- Entrepreneurial by nature
- Exceptional interpersonal and communication skills with ability to facilitate and solve problems
- Ability to organize, prioritize and coordinate multiple activities often under tight timelines
- Ability to drive projects to completion and achieve goals
- Strong judgment
- Team-player with collaborative spiritUnwavering ability to handle and maintain confidentiality regarding firm information, projects,and client data
- Ability to lead through influence and develop talent
- Proactive, analytical mindset
- Effective presentation skills
- Highly collaborative, transparent, and agile; able to reprioritize quickly in a fast-paced environment
- Exceptional verbal and written communication, including technical and user documentation; skilled at presenting complex concepts clearly to all organizational levels
- Strong analytical, consultative, and strategic mindset with proven ability to drive enterprise AI and workflow initiatives
- Success in managing projects and people for technical programs; effective under pressure and committed to meeting deadlines
- Professional demeanor with strong customer service orientation; listens actively and responds appropriately to complex challenges
- Intellectually curious, innovative, proactive and results-driven
- Excellent organizational skills and ability to manage multiple priorities simultaneously
Cooley offers a competitive compensation and excellent benefits package and is committed to fair and equitable employment practices.
EOE.
The expected annual pay range for this position with a full-time schedule is $210,000 - $260,000. Please note that final offer amount will be dependent on geographic location, applicable experience and skillset of the candidate.
We offer a full range of elective benefits including medical, health savings account (with applicable medical plan), dental, vision, health and/or dependent care flexible spending accounts, pre-tax commuter benefits, life insurance, AD&D, long-term care coverage, backup care for children and/or adults and other parental support benefits. In addition to elective benefit options, benefited employees receive firm-paid life insurance, AD&D, LTD, short term medical benefits as well as 25 days of Paid Time Off (“PTO”) and 10 paid holidays each year. We provide generous parental leave and fertility benefits. New employees will attend a detailed benefit orientation to learn more about our many benefits and resources.
Salary Context
This $210K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Cooley 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 $212,160 based on 13,809 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($235K) sits 11% above the category median. Disclosed range: $210K to $260K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Cooley LLP AI Hiring
Cooley LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $260K - $260K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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