Interested in this AI/ML Engineer role at Norton Rose Fulbright?
Apply Now →About This Role
Job Description
At Norton Rose Fulbright, people thrive because of a shared commitment to quality, unity and integrity. The highly regarded law firm consistently receives recognition from Great Place to Work and Top Workplaces, two companies that specialize in assessing organizational culture. Teams collaborate across regions, value new ideas and deliver meaningful client solutions, supported by a culture that embraces ambition, development and shared success. With more than 3,000 lawyers and 3,000 business services professionals working together across 50 offices worldwide, this global law firm provides a full range of legal services to leading corporations and financial institutions operating in key markets and sectors.
About you
-------------
You are an accomplished attorney seeking to leverage your technical and commercial fluency in a global law firm. You are keen on not just leveraging AI for client delivery but rethinking how delivery gets executed at scale with the technology available. You are equally comfortable discussing workflows with attorneys, legal practice considerations with engineers, and translating ambiguity into commercial products. You bring a curious and conscientious mindset: focusing on client value while ensuring solutions are scalable, secure, and aligned with firm risk tolerance.
About the role
Norton Rose Fulbright has a long\-standing commitment to leveraging technology to enhance legal services and streamline operations. We are seeking a highly motivated and experienced *Senior AI \& Innovation Counsel* to play a central role in embedding AI into legal practice and driving adoption across the firm. This role sits within the US Innovation team and reports to the US Director, Head of Innovation, and collaborates closely with legal practice groups, IT, KM, and other business services functions. A successful candidate will bring strong legal practice background, a practical understanding of AI technologies, and the ability to lead a team as well as complex, cross\-functional initiatives.
This is a unique opportunity to help build an AI and Innovation lawyer function inside one of the world's leading law firms \- with organizational support, a growing team, and genuine executive sponsorship. The work is consequential, the problems are hard, and the potential to reshape how legal services are delivered is real.
This role is critical to translating the firm’s AI investments into real\-world impact. By combining legal judgment with practical innovation, the Senior AI \& Innovation Counsel ensures that solutions are not only built \- but trusted, adopted, and embedded into how legal work gets done.
Responsibilities include, but are not limited to:
---------------------------------------------------------
As a player\-coach, this is a hands\-on role where you will be focused on both your practice group and people leadership. Equally, you will build trusted relationships with attorneys and business service colleagues across the firm to drive alignment and support for the Innovation program’s initiatives.
Practice Integration
------------------------
- Embed within priority practice group(s) and support other Innovation teams and key Innovation initiatives by encouraging adoption of AI tools and workflows
- Validate AI outputs through a practiced legal lens, ensuring they meet professional, ethical, and client standards
- Translate attorney needs into actionable requirements for other innovation and legal engineering teams
- Integrate AI and agentic tools into existing legal workflows to improve efficiency, consistency, and quality
- Maintain a pipeline of practice driven priorities and use cases
- Contribute to a library of reusable workflow patterns and agentic playbooks that can be adapted across practices
Use Case Development and Delivery
-------------------------------------
- Identify, prioritize, and develop high\-value use cases based on real legal work and practice experience
- Decompose legal matter workflows into discrete tasks, decision points, and dependencies to identify where AI and agentic solutions can drive the greatest impact
- Parter with other Innovation functions and translate decomposed workflows into clear functional specifications: agent behaviors, guardrails, evaluation criteria, and success metrics
- Partner with attorneys, other Innovation teams, and Legal Practice Management to map matter lifecycles and pinpoint high\-leverage automation opportunities within each phase
- Support rapid prototyping and testing of new workflows and tools; and help iterate solutions from concept to scalable, production\-ready offerings
- Capture feedback and measurement loops from attorneys to continuously improve and validate solutions performance against attorney expectations and matter quality standards
Capability Building and Leadership
--------------------------------------
- Operate as a player\-coach, balancing hands\-on engagement with people leadership and development
- Manage and mentor a team of AI and innovation lawyers as the function scales
- Establish AI and Innovation lawyer best practices/standards, cross\-functional collaboration expectations and workflows, and quality practices for the team
- Partner with the Director of Innovation and senior leadership to shape the AI and Innovation lawyer roadmap and org design
- Lead with impact and client value, and help shape future commercial outcomes and team structures
Collaboration andStakeholder Engagement
---------------------------------------------------
- Build trusted relationships with partners and attorneys across practice groups
- Serve as an adoption conduit and be an interface between lawyers, Innovation, and technical teams
- Work directly with Innovation leadership, attorneys, practice leaders, and business stakeholders to co\-design solutions
- Partner with the AI Governance \& Program Manager, IT, and Info security teams to ensure solutions meet firm standards and governance requirements
Qualifications:
-------------------
- JD required
- Minimum seven years' experience as a practicing attorney or in KM/ Innovation within professional services, preferably at an AmLaw 100 in a practice group of strategic importance to Norton Rose Fulbright
- Demonstrated and deep understanding of legal workflows and how legal work is delivered in practice
- Proven experience with legal technology, project management, business planning, and team management
- Comfort operating in ambiguity: this function is still being defined, and you will help define and mature it
- Excellent communication skills and ability to translate business or legal requirements into technical solutions and workflows; able to present to senior partners and work alongside engineers in the same day
- Demonstrated ability to manage multiple projects, deliver results in a fast\-paced environment, and drive organizational behavior change
- Genuine broader market curiosity about AI and its implications for the future of legal practice – beyond simply familiarity with the tools
- Understanding of data privacy, security, and responsible AI considerations in a professional services context
- Experience developing scalable frameworks, playbooks, or reusable solution patterns
Norton Rose Fulbright US LLP is committed to providing employees with a comprehensive and competitive benefits package that supports you, your health, and your family. Benefit packages include access to three medical plans, dental, vision, life, and disability insurance. Employees can also access pre\-tax benefits such as health savings and flexible spending accounts. Norton Rose Fulbright helps provide financial security by allowing employees to participate in a 401(k) savings plan and profit\-sharing plans if eligible. Full\- time employees are eligible to access fertility benefits designed to support fertility and family\-forming journeys.
In addition to the Firm’s health and welfare benefits above, we offer a competitive paid time off plan, which provides a minimum of 20 days off based on your role and tenure with the firm. The firm offers a generous paid parental leave benefit allowing parents to take a minimum of 14 weeks of paid leave to bond with your newborn, or adopted child(ren). Employees are also entitled to 11 Firm holidays.
Norton Rose Fulbright US LLP is an Equal Opportunity Employer and complies with all applicable federal laws and their implementing regulations that require the collection and recording of certain data and information. The information we receive will not be used to make any decision regarding employment and will be kept separate from your application. Similarly, self\-identification information is kept confidential and used only in accordance with applicable federal laws and regulations. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Norton Rose Fulbright is committed to providing reasonable accommodation as an Equal Opportunity Employer to applicants with disabilities. If you require assistance or accommodation to complete your application, please contact [email protected]. Please provide your contact information and a description of your accessibility issue. We will make a determination on your request for reasonable accommodation on a case\-by\-case basis.
E\-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E\-Verify in its hiring practices to achieve a lawful workforce.
Equal Employment Opportunity
Norton Rose Fulbright US LLP will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the firm, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-1\.35(c).
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 Norton Rose Fulbright, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
Norton Rose Fulbright AI Hiring
Norton Rose Fulbright has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.