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About This Role
Weil seeks the best and brightest talent to join our Administrative Staff team. Our employees are the future of Weil so we invest in their training and development. Our environment embraces and encourages positive-minded professionals who seek to be challenged and acquire knowledge and skills in an atmosphere built on teamwork. Highly skilled and knowledgeable Administrative Staff assist our attorneys in successfully meeting and exceeding client needs and are vital to the overall success of the Firm.
Job Description
The Technology Architecture & Engineering Department is responsible for worldwide technical strategy, design, implementation, and top level support for all core infrastructure. As part of TA&E, the Systems Engineering group is responsible for integrating various technologies including server OS, virtualization, storage, directories, email, security, and cloud platforms.
We are looking for an experienced and self-motivated cloud engineer. The engineer should have in-depth experience architecting, implementing, optimizing and troubleshooting enterprise scale server environments including cloud based, hybrid and on-prem deployments. This involves all aspects of architecting the environment; sizing, stability, resiliency, security, growth planning, monitoring, etc.
In relation to cloud responsibilities, the engineer will assist in further securing the existing cloud presence and expanding new workloads in the Azure and AWS adhering to best practices.Primary Responsibilities and/or Essential functions:
- Research, evaluate, and recommend technical solutions to address business challenges
- Architect, engineer, and deploy to production technical solutions including detailed architectural design documentation
- Transition systems from engineering to operations by providing documentation and training to technical staff
- Lead technical projects and sub-projects across the department
- Serve as subject matter expert and internal consultant to other technical staff and groups
- Be the top level responsible party for escalated support, working directly with external vendors and consultants to resolve issues and produce root cause analysis reports
- Efficiently triage and prioritize tasks, projects and issues in a highly demanding environment
- Any additional responsibilities as required by management.
Knowledge, Skills & Abilities:
- Expert understanding of cloud computing and design, IAM, VPC/Azure Landing Zone, and Virtual Networks
- Expert Azure infrastructure knowledge and experience required; some AWS and/or GCP experience desired
- Cloud certification a plus
- Expert understanding of implementing proper health/status monitoring for mission critical systems
- Solid understanding of PowerShell and general scripting/automation
- Solid understanding of encryption and security best practices and methods
- Experience troubleshooting with logs in SIEM, SCOM, and other tools
- Experience with on-premises hyperconverged environments a plus
- Good understanding of connectivity in the cloud
- 10+ years of experience in information technology, including 5+ years of experience in engineering and 3+ years of experience in a technical subject matter
- Proven experience with large enterprise scale environments
- Proven desire and ability to learn new technologies
- Proven success in leading projects
- Proven focus on customer service and business oriented thinking
Education/Certifications:
- Bachelor's degree in computer science, engineering, mathematics, physics, or a similar field
Estimated salary range is $150,000 - $200,000 .Compensation decisions within the range are dependent on the facts and circumstances of each case including but not limited to the individual's skills, experience, and qualifications.
Weil, Gotshal & Manges LLP offers a competitive compensation package comprised of base pay and discretionary year-end bonus for eligible employees. Benefits include, but are not limited to medical, dental, vision, disability coverage, life insurance, flexible spending plan and a 401K plan. Weil also offers generous paid time off and holidays. All decisions affecting employment at Weil, Gotshal & Manges LLP are made on the basis of qualification, performance and other pertinent work-related factors, and without discrimination against any person on the basis of race, color, sex, age, religion, national origin, disability, marital status, sexual orientation, gender identity or expression, pregnancy, veteran’s status, genetic information or any other legally protected status.
Salary Context
This $150K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Weil, Gotshal & Manges 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Weil, Gotshal & Manges LLP AI Hiring
Weil, Gotshal & Manges LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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