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About This Role
About Latham \& Watkins:
Latham \& Watkins is one of the world’s leading global law firms advising the businesses and institutions that drive the global economy. We are the market leaders in major financial and business centers around the world. Our investment in people, commitment to innovation, and focus on the future empower you to build an incredible career and thrive as an exceptional professional in a supportive culture. If you aspire to be the best, and work with the best, this is where you belong.
About the Role:
The Senior Manager of Technical AI Delivery is an integral part of Latham’s Technology \& Information Services team. This role will be responsible for leading complex, high\-impact AI\-focused technology initiatives across the entire lifecycle of software development, driving robust and scalable implementation of full\-stack applications for ML/GenAI applications, while driving the design and development of AI/ML applications across onshore and offshore delivery teams, ensuring alignment with business requirements.This role will be located in our Global Services Office located in downtown Los Angeles. Please note that this role may be eligible for a flexible working schedule that allows for a hybrid and in\-office presence.
Responsibilities \& Qualifications:
Other key responsibilities include:* Contributing to the entire lifecycle of application development including concept, design, test, and release
- Conducting high\-level technical reviews of code and pull requests from onshore/offshore engineering teams to ensure alignment with solution architecture, adherence to best practices and application requirements
- Translating business goals into technical requirements and architecture documents
- Driving technical planning sessions, design reviews, delivery checkpoints, and operational readiness reviews
- Setting and reinforcing best practices for software quality, documentation, testing and delivery along with scalability, reliability, security, compliance, maintainability, and user adoption
- Protecting and maintaining any highly sensitive, confidential, privileged, financial, and/or proprietary information that Latham \& Watkins retains
We’d love to hear from you if you:
- Display proven experience delivering enterprise\-scale AI, ML, and Generative AI solutions
- Possess demonstrated success leading delivery through distributed onshore/offshore engineering teams
- Demonstrate proficiency with Python including experience with libraries and frameworks relevant to ML/GenAI and agentic application development (e.g., Microsoft Agentic Framework)
And have:
- A bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or equivalent
- A minimum of eight (8\) years of experience in technical delivery role
- A minimum of five (5\) years of experience in driving technical project delivery in an onshore/offshore mode
- A minimum of five (5\) years of experience in full stack AI/ML application delivery
- A minimum of three (3\) years of experience working an agile development environment
Benefits \& Additional Information:
Successful candidates will not only be provided with an outstanding career opportunity and welcoming environment, but will also be provided with a generous total compensation package with bonuses awarded in recognition of both individual and firm performance. Eligible employees can participate in Latham’s comprehensive benefit program which includes:
- Healthcare, life and disability insurance
- A generous 401k plan
- At least 11 paid holidays per year, and a PTO program that accrues 23 days during the first year of employment and grows with tenure
- Well\-being programs (e.g. mental health services, mindfulness and resiliency, medical resources, well\-being events, and more)
- Professional development programs
- Employee discounts
- Affinity groups, networks, and coalitions for lawyers and staff
Latham \& Watkins is an equal opportunity employer. The Firm prohibits discrimination against any employee or applicant for employment on the basis of race (including, but not limited to, hair texture and protective hairstyles), color, religion, sex, age, national origin, sexual orientation, gender identity, veteran status (including veterans of the Vietnam era), gender expression, marital status, or any other characteristic or condition protected by applicable statute.
Latham \& Watkins LLP will consider qualified applicants with criminal histories in a manner consistent with the City of Los Angeles Fair Chance Initiative for Hiring Ordinance (FCIHO). Please click the link below to review the Ordinance.
Please click here to review your rights under U.S. employment laws. \#LI\-JG2 \#MidSenior
Pay Range: USD $220,000\.00 \- USD $250,000\.00 /Yr.
Salary Context
This $220K-$250K range is above the 75th percentile 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 Latham & Watkins 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 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 ($235K) sits 7% above the category median. Disclosed range: $220K to $250K.
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.
Latham & Watkins LLP AI Hiring
Latham & Watkins LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $250K - $250K.
Location Context
AI roles in Los Angeles pay a median of $215,000 across 397 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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