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About This Role
Summary:
The AI Transformation \& Learning Associate Director is responsible for building and scaling Sidley’s firmwide AI enablement strategy — equipping lawyers and business professionals to confidently and responsibly use AI in their daily work. This role sits at the center of the Firm’s AI transformation, shaping how AI is adopted across practices, influencing leadership, and positioning Sidley as a market leader in responsible AI.
The role blends responsible AI governance, change management, instructional design, and executive communication to drive practical AI proficiency across the Firm. This leader will design and lead a best\-in\-class AI enablement program; create audience\-specific engagement programs; embed Responsible AI standards in all training; and represent Sidley externally with clients and at industry forums. The role partners with senior stakeholders across Legal, Compliance, and Technology, and collaborates with Learning and Development, Knowledge Management, and Information Security \& Privacy. The ideal candidate demonstrates polished executive presence and can deliver compelling presentations and demos to partners, Firm leadership, and clients.
Duties and Responsibilities:
- Develop and drive the AI enablement vision, roadmap, and operating plan; define learning objectives and proficiency levels by audience (attorneys, staff, leadership, practice groups) in direct support of firm\-approved AI tools in collaboration with the Knowledge Management and Learning and Development teams.
- Organize, grow, and build engagement programs: internal communications, adoption campaigns, practice\-tailored playbooks, a network of AI Champions, and a community of practice.
- Act as a subject matter expert working with internal teams to drive the design and production of curricula (101 through advanced) utilizing standard firm messaging channels and platforms, including live workshops, e\-learning modules, microlearning, simulations, office hours, and a searchable content library.
- Partner with Learning \& Development to deliver content via the Firm’s LMS; track completions, assessments, CLE eligibility, and certification pathways where applicable.
- Coordinate with Product Management and Engineering on AI tool rollouts; help ensure enablement content (prompting standards, configuration tips, use\-case guides) is current and consistent with AI enablement messaging.
- Coordinate with internal teams to develop adoption and impact metrics.
- As needed, partner with Marketing and Communications teams to create client\-facing materials and present Sidley’s AI enablement and Responsible AI approach to clients; partner with Marketing/BD for proposals, thought leadership, and events.
- Deliver polished presentations to partners, committees, leadership, and client audiences.
- Maintain awareness of developments in generative AI, legal technology, and AI governance and regulation; translate changes into timely updates to policies, playbooks, and curricula.
- Participate in authoring practical guidance (prompting standards, model selection, risk triage workflows, data retention) and ensure accessibility for global audiences.
Education and Experience:
Required:
- Bachelor’s degree in a liberal arts, technology, or related field; or equivalent work experience.
- A minimum of 7 years of relevant professional experience.
- Demonstrated experience creating curricula and teaching technical topics to non\-technical audiences.
- Strong facilitation skills and polished executive/client presentation skills.
- Hands\-on expertise with AI platforms (e.g., OpenAI, Anthropic) and prompt\-engineering and RAG concepts.
Preferred:
- Experience with program measurement and analytics; familiarity with BI tools (Power BI/Tableau) or light scripting (e.g., Python) is a plus.
Other Skills and Abilities:
The following will also be required of the successful candidate:
- Strong organizational skills
- Strong attention to detail
- Good judgment
- Strong interpersonal communication skills
- Strong analytical and problem\-solving skills
- Able to work harmoniously and effectively with others
- Able to preserve confidentiality and exercise discretion
- Able to work under pressure
- Able to manage multiple projects with competing deadlines and priorities
\#LI\-HM1
\#LI\-Hybrid
Applicants must be authorized to work in the United States without the need for employer sponsorship, now or in the future
The target salary range for this role is:
$185,000 \- $225,000 if located in Illinois
Salaries vary by location and are based on numerous factors, including, but not limited to, the relevant market, skills, experience, and education of the selected candidate. Our compensation package also includes bonus eligibility and a comprehensive benefits program. Benefits information can be found at Sidley.com/Benefits .
To perform this job successfully, an individual must be able to perform the Duties and Responsibilities above satisfactorily and meet the requirements. The requirements listed above are representative of the minimum knowledge, skill, and/or ability required. Reasonable accommodations will be made to enable individuals with disabilities to perform the essential functions of the job. If you need such an accommodation, please email [email protected] (current employees should contact Human Resources).
Sidley Austin LLP is an Equal Opportunity Employer.
Salary Context
This $185K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Sidley Austin, 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($205K) sits 15% above the category median. Disclosed range: $185K to $225K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Sidley Austin AI Hiring
Sidley Austin has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $225K - $225K.
Location Context
AI roles in Chicago pay a median of $202,000 across 283 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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