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About This Role
Job Description Summary:
Join Fenwick's AI Innovation team to build and deploy the next generation of AI-powered solutions across the firm.
This role is focused on turning real business opportunities into production-ready AI applications, agents, and workflows that improve how attorneys and business professionals work. You will partner directly with stakeholders to identify high-impact use cases, design practical solutions, and deliver systems that drive measurable results.
The ideal candidate is an experienced software engineer with a builder mindset, strong product instincts, and a passion for applying emerging AI technologies to solve meaningful business problems.Job Description:
Build AI Solutions That Deliver Business Value
- Design, develop, and deploy AI-powered applications, agents, and workflow automations that solve real business challenges.
- Partner directly with attorneys and business teams to understand needs, define requirements, and translate opportunities into working solutions.
- Rapidly prototype concepts, validate value, and evolve successful solutions into production-ready systems.
- Identify opportunities to improve productivity, knowledge access, client service, and operational efficiency through AI.
Own Solutions End-to-End
- Lead the technical implementation of AI projects from concept through production deployment.
- Integrate AI capabilities with internal systems, knowledge repositories, and business applications.
- Develop reliable, secure, and maintainable solutions that can scale across the organization.
- Monitor solution performance and continuously improve quality, accuracy, and user experience.
Advance the Firm's AI Capabilities
- Evaluate emerging AI models, frameworks, tools, and platforms and determine their practical application within the firm.
- Contribute to reusable patterns, frameworks, and components that accelerate future development.
- Help establish engineering best practices for agent development, evaluation, deployment, and operations.
- Stay current on advancements in AI, agentic systems, orchestration frameworks, and enterprise AI technologies.
Collaborate Across the Organization
- Work closely with Innovation, IT, Knowledge Management, and Business Operations teams to deliver impactful solutions.
- Communicate technical concepts clearly to non-technical audiences.
- Support adoption of solutions through documentation, demonstrations, and stakeholder engagement.
- Serve as a trusted technical advisor on AI implementation opportunities.
Desired Skills and Qualifications:
- 5+ years of software engineering experience building and deploying business applications.
- Experience developing applications using modern programming languages such as Python, JavaScript, TypeScript, or similar technologies.
- Experience integrating APIs, data sources, and enterprise systems.
- Strong problem-solving skills and an ability to translate business requirements into technical solutions.
- Excellent communication and collaboration skills.
- Demonstrated curiosity and enthusiasm for AI technologies and their practical application.
Experience
- Experience building AI-powered applications using large language models and modern AI platforms.
- Familiarity with agent architectures, retrieval systems, orchestration frameworks, or workflow automation platforms.
- Experience with cloud platforms and modern software delivery practices.
- Experience evaluating and improving application performance, reliability, and quality.
- Experience working directly with business stakeholders throughout the product development lifecycle.
Benefits and Compensation Details:
At Fenwick, we believe that our partners and employees are our most important asset. Helping you and your families achieve and maintain good health - physical, emotional, and financial - is the reason we offer a comprehensive benefit program. We provide benefits to eligible employees under plans such as Healthcare, Life Insurance, Health Savings Accounts, Flexible Spending Accounts, and Wellbeing. Except as otherwise required by law, temporary employees are generally not entitled to these benefits.
The anticipated range for this position is:
$126,000 - $189,000 Depending on the role, the actual base salary offered may depend upon a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location in which the applicant lives and/or from which they will be performing the job.
A discretionary bonus for eligible employees may also be available based on performance. Additional information about employee eligibility for benefits and discretionary bonus will be made available upon request.
Salary Context
This $126K-$189K range is below 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 Fenwick & West, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($157K) sits 26% below the category median. Disclosed range: $126K to $189K.
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.
Fenwick & West AI Hiring
Fenwick & West has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Silicon Valley, CA, US. Compensation range: $189K - $189K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 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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