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Opening Type:
Business Professionals
Department:
Information Technology
Employment Type:
Full\-Time
Fully Remote or Hybrid (Business Professionals Only)
Location:
Austin
Boston
Boulder
Century City
Los Angeles
New York
Palo Alto
Salt Lake City
San Diego
San Francisco
Seattle
Washington, D.C.
Wilmington
Wilson Sonsini is the premier legal advisor to technology, life sciences, and other growth enterprises worldwide. We represent companies at every stage of development, from entrepreneurial start\-ups to multibillion\-dollar global corporations, as well as the venture firms, private equity firms, and investment banks that finance and advise them. The firm has approximately 1,100 attorneys in 17 offices: 13 in the U.S., two in China, and two in Europe. Our broad spectrum of practices and entrepreneurial spirit allow exceptional opportunities for professional achievement and career growth.
The AI Systems Manager (ASM) will help lead the firm’s enterprise artificial intelligence platform function, with direct responsibility for the administration, adoption and governance of the firm’s AI systems at enterprise scale. The ASM oversees the full range of firm\-wide AI tools, including chat\-based, agentic and developer\-facing products, and is accountable for ensuring these platforms remain secure, well\-governed, cost\-effective and broadly adopted throughout the enterprise.
The ASM has deep experience managing enterprise software platforms at scale and leading technical, analytical and support professionals through the full lifecycle of platform administration, from provisioning and licensing to usage monitoring, spend management and end\-user support. The ASM will lead the AI Systems team, comprised of analytics, intake and user support functions, to ensure the firm’s AI investment translates into measurable adoption, productivity and business value. This leadership role is self\-directed, people\-oriented and comfortable supporting the AI and technology needs of the organization, clients, attorneys, practice groups and administrative teams.
The ASM will work closely with IT Operations, Security, Risk, Change Management, Program Management, Project Management, Applications, Data Operations, Data Science and Innovation teams to enforce AI\-related policies, procedures and controls, ensuring that platform growth and adoption remain aligned with the firm’s governance, security and risk\-management obligations.
Essential Duties and Responsibilities:
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AI Platform Administration \& Enterprise Management
- Administer and maintain the firm’s enterprise AI platforms, ensuring reliable, secure and scalable access at enterprise scale, firm\-wide.
- Manage user provisioning, licensing, roles and permissions across all AI platform surfaces, including chat\-based, agentic and developer\-facing tools.
- Own the platform roadmap for enterprise AI tools, coordinating upgrades, feature rollouts, integrations and configuration changes with minimal disruption to end users.
- Serve as an IT point of contact and account owner for AI platform vendors, managing vendor relationships, contract renewals, support escalations and service\-level commitments.
- Triage technical and support issues related to AI platform access, performance and functionality, working with vendor support teams and internal IT groups to troubleshoot, resolve and document issue resolution.
User Enablement, Training \& Adoption
- Enable and support the Program Management and Project Management teams with onboarding, training and adoption programs tailored to the firm’s distinct AI user populations, including business professionals, cross\-functional teams and developers and engineers using the firm’s AI platforms.
- Create and maintain documentation, playbooks and best\-practice guidance to further the firm’s AI systems and platforms.
- Partner with practice groups, administrative departments and firm leadership to identify high\-value AI use cases and drive adoption of the firm’s AI platforms across the enterprise.
- Establish feedback channels and user\-support processes to continuously improve the AI user experience and address emerging needs.
Spend, Usage \& Analytics Management
- Own and manage the AI platform budget, tracking licensing costs and usage\-based spend, and report on budget performance, variances and forecasts to IT and firm leadership.
- Develop and maintain usage analytics and reporting dashboards that provide visibility into adoption, utilization, cost\-per\-user and return on investment across all AI platforms and user groups.
- Analyze usage trends to identify underutilized licenses, optimization opportunities and areas for expanded investment, and make data\-driven recommendations on platform scaling.
- Partner with the budget teams and firm leadership to develop cost\-allocation models and support annual budgeting and forecasting for the firm’s AI technology investments.
Governance, Policy \& Cross\-Functional Compliance
- Work closely with IT Security and Risk teams to enforce AI usage policies, data\-handling guidelines and access controls that protect firm and client confidential information.
- Partner with Program Management teams to plan, communicate and manage the rollout of new AI features, platform changes and policy updates across the firm.
- Coordinate with Program Management and Project Management teams to align AI initiatives with the firm’s broader technology roadmap and portfolio of projects.
- Collaborate with the Applications, Data Operations, Data Science and Innovation teams to integrate AI platforms with the firm’s broader data and application ecosystem, and to identify opportunities to extend AI capabilities into new workflows.
- Maintain and enforce compliance with firm policies, procedures and applicable regulatory or client\-driven requirements governing the responsible use of artificial intelligence.
- Support internal and external audits, security reviews and risk assessments related to the firm’s AI platforms and their use.
Managerial Duties and Responsibilities
- Manage, mentor and support the AI Systems team, including analytics, intake and user support functions, so that each function operates at capacity and delivers productive work toward the team’s priorities.
- Lead the AI Systems team in project planning, execution and analysis, including intake triage, prioritization, testing, training, documentation and skill development.
- Collaborate with managers and colleagues throughout the IT Department and the firm to set goals and priorities for the AI Systems team that best support the firm’s business objectives.
- Establish and manage the annual AI platform budget, and report actual spend for tracking the budget throughout the year, including new spending requirements or other budget variations.
- Develop and refine the intake process for new AI use cases, requests and pilots, ensuring requests are properly scoped, prioritized and routed to the appropriate resources.
- Liaise with external vendors and consultants who support the firm’s AI platforms and related services.
- Manage AI\-related invoices, bill payments and other internal and external communications to ensure appropriate payments are made accurately and on time.
Experience, Knowledge and Abilities
- Ability to communicate clearly and effectively with people from both technical and non\-technical backgrounds. Excellent writing and oral presentation skills.
- Proven ability to lead a team through all facets of platform management, from strategy and planning to operational execution and measurable business outcomes.
- Extensive experience administering enterprise SaaS or cloud\-based platforms at scale, including user provisioning, licensing, configuration and lifecycle management.
- Working knowledge of leading generative AI platforms and large language model tools, and their practical application in a professional\-services environment.
- Strong analytical skills, with experience building usage dashboards, spend reports and adoption metrics to communicate platform performance to technical and non\-technical stakeholders alike.
- Experience developing and managing technology budgets, vendor contracts and service\-level agreements.
- Experience building or supporting intake and triage processes for technology requests, projects or use cases.
- Ability to work effectively with cross\-functional IT and other administrative department teams, including Innovation, IT, Marketing, KM, HR and Finance to enforce policy and drive compliance.
- Ability to deal responsibly with sensitive and confidential information in a discreet and secure manner.
- Ability to collaborate with team members and interact with others throughout the Firm.
- Familiarity with data privacy, information security and responsible\-AI governance principles, including model risk, data residency and access controls.
- Law firm or professional\-services experience a plus.
Requirements
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- 7\+ years of experience managing enterprise technology platforms, SaaS applications, or AI/ML systems, including 2\+ years directly managing a team.
- Proven experience leading platform adoption, governance and support functions for a large, enterprise\-scale organization.
- BS and/or graduate degree in Computer Science, Information Systems, Business Administration or equivalent discipline, or equivalent practical experience.
- Experience managing technology budgets, vendor relationships and usage analytics/reporting.
- Excellent verbal and written communication and interpersonal skills.
The primary location for this job posting is in Palo Alto, but other locations may be listed. The actual base pay offered will depend upon a variety of factors, including but not limited to the selected candidate’s qualifications, years of relevant experience, level of education, professional certifications and licenses, and work location. The anticipated pay range for this position is as follows:
Palo Alto, New York, San Francisco: $163,200 – $220,800 per year.
Austin, Boston, Boulder, Century City, Delaware, Los Angeles, Salt Lake City, San Diego, Seattle, Washington, D.C., and all other locations: $147,050 – $198,950 per year.
The compensation for this position may include a discretionary year\-end merit bonus based on performance. We offer a highly competitive salary and benefits package.
Equal Opportunity Employer (EOE).
Salary Context
This $147K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Wilson Sonsini Goodrich & Rosati, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($183K) sits 14% below the category median. Disclosed range: $147K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Wilson Sonsini Goodrich & Rosati AI Hiring
Wilson Sonsini Goodrich & Rosati has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $220K - $220K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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