Interested in this AI/ML Engineer role at Freshfields?
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Overview of the Firm and Function
Freshfields is a global law firm, providing business law advice of the highest quality. We want to be the law firm that clients turn to for legal advice where it most matters, wherever in the world that may be. The Firm has over 2,400 lawyers in 28 offices around the world, providing a comprehensive service to national and multinational corporations, financial institutions and governments.
Our people make our firm \- we are a people business and want to create a welcoming and supportive environment where all can flourish. We see diversity as a strength which creates fresh perspectives and generates new ideas. We enjoy our work and are determined to do an outstanding job. We deliver best when working in teams.
We think and work globally \- we don’t just say we are one firm; we act as one firm right across the world. We work wherever our clients need us. This is how we define ourselves, not by reference to where we have offices. Cross\-border work isn’t just what we do, it is what we excel at. We understand what it really takes to work across different legal systems and commercial environments and to bridge language and cultural gaps.
We aim to add value in everything we do \- we are passionate about helping our clients succeed. We use our experience and creativity to help clients make judgements and achieve their goals. In everything we do, we seek to make a real difference to the communities in which we operate.
About Marketing \& Business Development (MBD)
MBD is a dynamic global team which includes business development specialists, research analysts, brand, marketing and communications experts. By working collaboratively, our team shapes the firm’s client strategy, completes analysis of complex business issues, targets opportunities, develops compelling proposals and creates content and marketing campaigns that lead our digital presence. Together, we define the client experience and shape the perception of Freshfields around the globe.
Key responsibilities and deliverables
The US Manager, BD Pursuits \& AI Enablement is a US\-based role focused on raising the quality, consistency, and speed of how the firm pursues and wins work. Day to day, that means two core priorities: first, owning and streamlining the US pitch process with AI enablement, working closely with the BD teams; and second, leveraging AI to develop and manage the global pitch content library/repository. This encompasses the content, experience data, and process infrastructure behind US pitches, proposals, awards, and directory submissions, and building the AI\-enabled workflows that help BD teams move faster and more effectively on live opportunities.
This role works closely with the Global MBD Operations Systems team, including global experience and CRM, and the Market Intelligence and global Business Development teams, to ensure US practices are consistent with firm\-wide standards and tools.
This role is suited to someone who knows pursuit work well and wants to transform how it gets done. You bring strong BD knowledge and operational discipline, and understand how AI tools can be applied to the specific, high\-friction moments in the pursuit process: building credentials, finding the right matter experience, drafting a pitch under time pressure, managing a directory submission.
What you'll do
- Sort out and streamline the US pitch process end to end, embedding AI enablement at each stage and working closely with the BD teams to drive adoption and impact on live opportunities.
- Leverage AI to develop and manage the global pitch content library/repository, ensuring it is comprehensive, current, well\-structured, and easy to search, retrieve, and reuse across teams.
- Own the content, experience data, and process infrastructure behind US pitches, proposals, awards, and directory submissions, with a focus on quality, consistency, and speed.
- Build and maintain the shared data spine that powers AI\-enabled pursuit work, including pitch content, matter experience, credentials, and lawyer bios, ensuring information is complete, current, and structured for AI retrieval and reuse.
- Develop and maintain reusable pursuit assets, including pitch modules, matter descriptions, credential sets, templates, and prompt frameworks, so teams spend their time on strategy and tailoring, not building from scratch.
- Partner with BD, lawyers, and business services teams to ensure experience data and CRM records are accurate, current, and complete enough to power AI\-drafted pitches and submissions.
- Identify high\-friction points in the pursuit workflow and translate them into clear, specific improvement briefs with measurable outcomes.
- Develop best practices, training materials, prompt libraries, and process documentation to support consistent execution and user adoption across the US team.
- Support US Chambers and other legal directory submissions, including process management, stakeholder coordination, and quality control.
- Provide hands\-on support on priority pitches and submissions, applying AI tools to support drafting, content refinement, and knowledge retrieval.
*For individuals assigned and/or hired to work in New York and California or reporting to someone in those states, Freshfields is required by law to include a reasonable estimate of the compensation range for this role. This compensation range is specific to the States of New York and California and takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $140,000 to $180,000\.*
*EEO Statement*
*Freshfields US LLP is proud to be an equal employment employer. Our policies and practices will be free from unlawful discrimination based upon race, color, ethnicity, religion, creed, sex (including pregnancy, childbirth or related medical conditions), national origin, citizenship, immigration status, ancestry, age, marital status, protected veteran status, military service, disability, medical condition, genetic information, sexual orientation, gender identity, or any basis prohibited under federal, state or local law. We strive to promote an atmosphere that encourages equal opportunities and prohibits discriminatory practices, including sexual harassment.*
*Disability Accommodation for Applicants to Freshfields US LLP*
*Freshfields US LLP is an Equal Employment Opportunity employer and provides reasonable accommodation for qualified individuals with disabilities and disabled veterans in job application procedures. If you have any difficulty using our online system and you need an accommodation due to a disability, you may use the alternative email address below to contact us about your interest in employment at* *[email protected]* *, or you can send your resume to* *[email protected]* *, or you can call us at \+1\-212\-277\-4000\.*
Salary Context
This $140K-$180K range is below 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 Freshfields, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $140K to $180K.
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.
Freshfields AI Hiring
Freshfields has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 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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