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Practice Systems & Applied AI: Legal AI Engineer, Litigation
Information Services Department
Debevoise & Plimpton LLP is a premier law firm with market-leading practices, a global perspective, and strong New York roots. Our clients look to us to bring a distinctively high degree of quality, intensity, and creativity to resolve legal challenges effectively and cost-efficiently. We believe in hiring talented and dedicated individuals as members of our administrative and professional community. We draw on the strength of our culture and structure to deliver the best of our firm to our lawyers and clients through true collaboration.
We are seeking a Legal AI Engineer, Litigation on our Practice Systems & Applied AI team to serve under the direction of the Head of Practice Systems & Applied AI in our Information Services Department. This individual will serve as a senior litigation practice and client advisor responsible for helping lawyers and clients understand, evaluate, and apply artificial intelligence to practice groups in the Litigation Department.
The Legal AI Engineer will lead client-facing AI discussions, identify opportunities within disputes, investigations, enforcement matters, regulatory proceedings, and other contentious matters, and translate complex AI concepts into practical guidance for lawyers, clients, and Firm leadership. The role requires the judgment and credibility to engage directly with Partners, Counsel, Associates, and clients on how AI can be used responsibly to improve litigation legal workflows and client service. This position is intended for an experienced attorney or legal innovation professional with strong knowledge of litigation legal practice and exceptional communication and presentation skills.
RESPONSIBILITIES INCLUDE, BUT ARE NOT LIMITED TO:
- Collaborate with the Head of Practice Systems & Applied AI to execute the Firm’s AI strategy and expand the responsible use of AI across litigation practices and client engagements.
- Independently prepare for and lead client-facing presentations, meetings, demonstrations, trainings, and continuing legal education programs on AI concepts, capabilities, risks, and legal use cases.
- Serve as a subject-matter expert for lawyers engaging with clients on AI, helping them clearly explain the Firm’s capabilities, approach, governance, and experience.
- Advise litigation clients and matter teams on the practical application of AI within legal departments and matter teams, including use-case development, workflow design, adoption considerations, and responsible-use practices.
- Partner directly with Partners, Counsel, and Associates in the Litigation Department to understand legal workflows, identify high-value AI opportunities, and develop practical approaches for integrating AI into litigation client work.
- Apply knowledge of litigation legal practice to assess how AI may support drafting, review, discovery, research, case management, investigations, enforcement matters, regulatory proceedings, and other litigation processes.
- Serve as a hands-on resource for lawyers seeking to use AI on specific matters, including guidance on use-case selection, workflow design, prompt development, quality control, and attorney review.
- Develop litigation practice-specific AI guidance, demonstrations, use cases, talking points, playbooks, and training materials for lawyers and client teams.
- Support pitches, requests for proposals, client alerts, panel discussions, and other business development efforts by contributing AI-related content and presenting the Firm’s approach to legal AI.
- Educate attorneys on how to speak with clients about AI accurately and credibly, including how to discuss capabilities, limitations, risks, governance, and potential value.
- Translate litigation and client needs into clear business and functional requirements for the Applied AI Engineering & Solutions team and other technical stakeholders.
- Support the evaluation and rollout of new AI tools by assessing their relevance to legal practice, developing adoption strategies, and helping lawyers incorporate approved capabilities into their work.
- Contribute to the Applied AI roadmap by identifying litigation practice and client priorities, evaluating potential impact, and recommending initiatives for research, development, or implementation.
- Prepare communications, briefings, and recommendations for Firm leadership and other stakeholders regarding AI adoption, litigation client and matter activity, practice opportunities, and emerging legal-industry developments.
- Stay abreast of advances in AI, legal technology, legal-service delivery, client expectations, professional responsibility, and industry practices to ensure the Firm’s guidance remains current and practical.
QUALIFICATIONS:
- J.D. required; minimum of three years working as a practicing attorney, legal innovation professional, knowledge management attorney, practice support attorney, or similar law firm professional.
- Demonstrated knowledge of litigation legal practices, litigation matter workflows, attorney work product, and the delivery of legal services in a litigation practice context.
- Proven ability to independently lead presentations, meetings, trainings, and substantive discussions with clients, lawyers, and senior stakeholders.
- Experience advising lawyers or clients on AI, legal technology, knowledge management, innovation, legal operations, or technology-enabled legal-service delivery.
- Strong working knowledge of generative AI, large language models, prompt design, AI-assisted legal workflows, and the opportunities and limitations of AI in legal practice.
- Experience developing legal or business requirements and working with technical teams to design, evaluate, or implement technology solutions.
- Experience supporting client development, pitches, requests for proposals, client training, or other business development initiatives preferred.
- Understanding of confidentiality, privilege, data governance, professional responsibility, privacy, and ethical considerations associated with the use of AI in legal services.
- Proven track record of leading complex, cross-functional initiatives from initial opportunity identification through implementation, adoption, and ongoing support.
REQUIREDSKILLS:
- Exceptional presentation and public-speaking skills, with the ability to lead credible and engaging discussions with clients, Partners, Firm leadership, attorneys, and professional staff.
- Deep understanding of litigation legal practice areas and the ability to analyze workflows across different litigation matter types, case workflows, investigations, discovery, research, and other litigation processes.
- Strong understanding of AI concepts and legal use cases, with the ability to explain capabilities, limitations, risks, and technical concepts in clear, practical language.
- Excellent written and verbal communication skills, including the ability to develop executive briefings, client materials, training content, demonstrations, and practice-specific guidance.
- Strong judgment, professional credibility, and interpersonal skills, with the ability to influence senior stakeholders and navigate sensitive client, practice, and organizational considerations.
- Ability to translate litigation and client needs into clear use cases, functional requirements, implementation priorities, and recommendations for technical teams.
- Strong organizational and project-management skills, including the ability to manage multiple litigation and client, practice, training, and implementation initiatives in a fast-paced environment.
TO APPLY:
A resume is required to apply for this position. Please tell us your salary requirements and where you saw this position posted. Send required materials to:
Human Resources [email protected] 212.909.8310
Debevoise & Plimpton LLP is an equal opportunity employer. All qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran or any other legally protected category in accordance with U.S. law.
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Salary Context
This $240K-$300K 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 Debevoise & Plimpton 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 $212,160 based on 13,809 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($270K) sits 27% above the category median. Disclosed range: $240K to $300K.
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.
Debevoise & Plimpton LLP AI Hiring
Debevoise & Plimpton LLP has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $105K - $300K.
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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