Interested in this AI/ML Engineer role at McDermott Will & Schulte?
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About This Role
Build your big career with the firm that does Big Law, Better. McDermott Will \& Schulte is a leading global law firm that brings together more than 1,700\+ lawyers and 1,400 business professionals. We celebrate excellence, collaboration, and community and have been recognized as a top workplace by *USA Today, Fortune*, *The American Lawyer, Vault* and others. We are also certified by Great Place to Work.
At McDermott, we don’t just invest in your future, we accelerate your career – wherever it may lead. That includes supporting you both in and outside of the office.
With us, you’ll find:
- A firm where everyone belongs: Our award\-winning culture prioritizes warmth and authenticity — we encourage you to be yourself!
- Enthusiasm for all perspectives: We’re smarter and stronger when everyone has a voice and a seat at the table. We welcome unique viewpoints and ideas, and we make opportunities for you and your career to thrive.
- Support to feel your best and do your best: Wellness is integral to building a successful career and a rich life. That’s why our benefits program supports your physical, emotional, mental, and financial health, with an emphasis on work\-life balance.
- Real rewards for real work: We offer generous compensation packages that recognize hard work and excellence.
Job Description:
McDermott, Will \& Schulte LLP is building one of the most forward\-looking AI programs in the legal industry — and the financial intelligence layer beneath it demands equally ambitious leadership. The Senior Manager, AI \& Analytics owns the Accounting \& Finance Systems Reporting, Data \& AI Center of Excellence — driving strategy, governance, and delivery of analytics, intelligent automation, and AI capabilities that power the firm’s financial operations. This is a senior leadership role: build the roadmap, set the standards, lead the program, and deliver measurable impact.
WHAT YOU’LL DO
- Lead the Reporting, Data \& AI Center of Excellence — establishing strategy, governance, operating standards, and a multi\-year roadmap for the function
- Develop and maintain a prioritized portfolio of reporting, analytics, automation, and AI initiatives across the Accounting \& Finance organization
- Define and govern reporting standards, KPI definitions, and semantic models — driving data stewardship, self\-service analytics, executive dashboards, and enterprise intelligence
- Own the Accounting \& Finance AI program end\-to\-end — intake, prioritization, governance, implementation, training, adoption, and value realization
- Identify opportunities to apply AI, machine learning, and intelligent automation across Contract\-to\-Cash, Invoice\-to\-Pay, and Plan\-to\-Report
- Establish responsible AI governance including controls, transparency, auditability, risk management, and compliance
- Partner with Information Technology on application strategy, integrations, security, data architecture, and governance
- Lead governance forums to prioritize initiatives, monitor progress, and measure business outcomes
- Develop and mentor team members while fostering collaboration, accountability, and innovation
WHAT YOU BRING
- 10\+ years of progressive experience in analytics, reporting, financial systems, automation, AI, or digital transformation
- 3\+ years of leadership experience, with a track record of building and developing high\-performing teams
- Proven ability to establish governance frameworks, operating models, and strategic roadmaps
- Strong command of accounting principles, financial operations, reporting, internal controls, and data governance
- Working knowledge of generative AI, intelligent automation, prompt engineering, and responsible AI practices
- Exceptional ability to translate complex business requirements into scalable technology solutions — communicating equally well to a CFO and an engineer
- Outstanding communication, presentation, stakeholder management, and change leadership skills
- Bachelor’s degree in Accounting, Finance, Information Systems, Business Administration, Data Analytics, Computer Science, or related field required
- Master’s degree, MBA, CPA, CMA, or equivalent advanced credential preferred
Preferred
- Professional services or legal industry experience
- Experience supporting enterprise financial systems and reporting environments
- Experience leading enterprise AI adoption or governance initiatives
- Background in systems integration, workflow automation, or modern application delivery
WHY MWS
- Lead one of the most ambitious AI programs in the legal industry — from the financial core outward
- Senior leadership scope — own the full stack from roadmap to delivery to governance
- Strategic partnerships across Accounting, Finance, IT, and executive leadership
- High\-impact programs — Contract\-to\-Cash, Invoice\-to\-Pay, Plan\-to\-Report — with executive sponsorship and real investment
Successful candidates will be provided with outstanding career opportunities and will receive a competitive total rewards package with the opportunity to earn performance\-based bonuses.
Target Hiring Range $158,000 \- $246,000
Please note that quoted salary ranges are not guarantees of what final salary offers may be. Base pay is based on market location and may vary depending on job\-related knowledge, skills, experience, and geographic location. Base pay is only one part of the Total Rewards that MWS provides to compensate and recognize our staff professionals for their work. Full time positions are eligible for a discretionary bonus and a comprehensive benefits package.
Physical Demands and Work Environment:
The physical demands and work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Physical Demands:
- While performing the duties of this job, the employee is required to sit, use hands, reach with hands and arms, stoop, talk and hear
- Employee must occasionally lift up to twenty (20\) pounds
Work Environment:
Typical indoor office environment
Disclaimer:
The above statements are intended to describe the general nature and level of the work being performed by people within this classification. They are not intended to be an exhaustive list of all responsibilities, duties and skills required of employees assigned to this job.
Salary Context
This $158K-$246K range is above 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 McDermott Will & Schulte, 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 $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 ($202K) sits 8% below the category median. Disclosed range: $158K to $246K.
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.
McDermott Will & Schulte AI Hiring
McDermott Will & Schulte has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $246K - $246K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below 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 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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