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About This Role
NetDocuments is committed to providing an excellent candidate experience and will never ask you to engage in recruitment activity without phone, video, and in person meetings and communications from emails using the @netdocuments.com domain. If you have any concerns or questions about communications you have received, please send them to [email protected] so our team members can review.
NetDocuments is the world's \#1 trusted cloud\-based content management and productivity platform that helps legal professionals do their best work. We strive to win together through passionate hard work, exploring new things and recognizing every interaction matters.
NetDocuments provides rewarding career growth in an inclusive, diverse environment where employees are encouraged to openly contribute creative ideas and innovation, backed by supportive peers and leadership working together to achieve our goals as a unified team.
At our core, we are dedicated to empowering our employees to drive successful business outcomes and better user experiences for our customers and partners. Our customer\-centric approach and employee enablement has allowed us to enjoy many accolades, including being named among the 2022, 2023, \& 2024 list of Inc. Magazine's 5000 Fastest\-Growing Private Companies in America.
Other recent awards include:
- Five\-time winner (2025, 2024, 2023, 2022, 2021\) Top Workplace in the US by the Salt Lake Tribune
- Four\-time winner (2025, 2023, 2022, 2021\) Best Companies to Work for by Utah Business magazine
- Three\-time winner (2025, 2023, 2022\) Top Workplace Employee Appreciation and Employee Well\-Being
- Three\-time winner (2024, 2023, 2022\) Top Workplace Work\-Life Flexibility
- Three\-time winner (2024, 2023, 2022\) Top Workplace Compensation \& Benefits
- Two\-time winner (2024, 2023\) National Top Workplaces
- Two\-time winner (2024, 2023\) Top Workplace Innovation
- 2025 Remote Work
- 2024 Technology Industry
NetDocuments is a hybrid, remote\-friendly workplace. Come join our team and work inspired each day!
What You'll Do
The NetDocuments AI Assistant, part of ndMAX, is our flagship legal AI product: secure, DMS native AI that keeps a firm's documents inside NetDocuments while helping legal professionals analyze documents, draft and refine content, and take action through natural language, embedded where they already work, in Word, Outlook, and search. A strong security posture and deep integration with the system of record are central to how the product earns trust with legal customers.
We are building the next generation of the Assistant's capabilities: intelligent, agentic AI that can take actions on a user's behalf, specialized agents for common legal tasks, and secure integrations, including support for the Model Context Protocol, that let the Assistant work across the tools legal professionals rely on.
We are seeking an Engineering Manager to lead the AI Assistant engineering team through this next chapter. The work is live. We are shipping to real customers and growing the team, and you will join at a formative moment for the product, when leadership decisions, technical bets, and team building habits compound for years.
This is one of the most strategically important investments NetDocuments is making, and the category is moving fast. The Engineering Manager who leads this team will help shape what the AI Assistant becomes, and grow the leadership skills and experience that pave the way for future opportunities.
A few principles shape this work and the candidates we're looking for in this role:
- Validate, then scale. We ship to customers early, learn fast, and harden as features prove their value.
- AI forward. We design around what modern models and agents make possible.
- Customer feedback drives the roadmap. What we build next is shaped by what customers tell us matters most.
- Security and trust first. We build AI that respects each firm's permissions and keeps sensitive content protected on the system of record.
In this role, you will:
Lead and develop a high performing team
- Lead, coach, and develop a team of approximately 5 to 6 software engineers and software test engineers.
- Foster a culture of trust, collaboration, accountability, and continuous improvement.
- Partner with recruiting to attract and hire exceptional engineering talent, including engineers with applied AI and machine learning experience.
- Support career growth through mentorship, feedback, and performance development.
- Create the conditions for engineers to do their best work, and be the escalation path for blockers the team cannot resolve on its own.
Drive delivery and execution
- Partner closely with Product, Design, and platform engineering teams to turn customer needs into scalable software solutions.
- Help build and execute a roadmap that balances customer facing impact, technical excellence, and long term platform durability.
- Guide your team through planning, execution, and delivery of the Assistant's agentic AI capabilities while holding a high bar for quality.
- Lead through ambiguity, sequencing the work, derisking it, and adjusting as we learn from real users and as the AI landscape evolves.
- Communicate capacity, priorities, and delivery risks proactively as a core member of the engineering, product, and design triad.
Collaborate cross functionally
- Coordinate across multiple engineering teams contributing to a shared product vision. A meaningful portion of this role is unlocking work that crosses team boundaries, including bringing the Assistant to customers across NetDocuments products and surfaces.
- Communicate priorities, risks, and progress clearly to technical and nontechnical stakeholders.
- Build the partnerships that drive alignment and execution across the organization.
Provide technical leadership
- Provide technical guidance and architectural input while empowering engineers to own implementation details.
- Champion modern engineering practices, including CI/CD, cloud native architectures on AWS, observability, automated testing, and scalable system design.
- Advocate for highly reliable, maintainable, and secure software, with strong observability and a proactive approach to security.
- Bring sound judgment to building on large language models and agentic systems, including evaluation, prompt and context design, tool integration, cost and latency management, and safe rollout.
Shape culture and innovation
- Foster a growth mindset and a culture of continuous learning.
- Encourage experimentation, thoughtful problem solving, and learning from incidents.
- Cultivate transparency, ownership, and customer impact across the team.
- Support knowledge sharing and technical excellence across teams.
What You'll Need to Be Successful
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- 7\+ years of software engineering experience.
- 2\+ years leading engineering teams, including both software engineers and software test engineers.
- Working familiarity with modern AI, including large language models and agentic systems, and sound judgment about applying them in production software. This is table stakes for the role.
- Experience building and delivering enterprise scale, customer facing software products.
- Strong understanding of modern software development practices, including cloud platforms, CI/CD, distributed systems, and scalable architectures.
- Proven ability to lead teams through ambiguity while balancing execution and technical quality.
- Excellent communication and stakeholder management skills.
- Demonstrated success building high performing teams and delivering impactful software.
What Will Make You Stand Out
- A track record of shipping AI native or agentic product surfaces at scale, features built around what large language models and agents make possible, not bolted onto an existing product.
- Depth in the applied AI stack: LLM application development, retrieval augmented generation, evaluation and observability for AI systems, and managing model cost, latency, and quality tradeoffs.
- Experience leading teams that build platform scale SaaS products, extensibility platforms, or developer and authoring tooling.
- Experience in legal technology, professional services SaaS, or other regulated enterprise software where data sensitivity and auditability matter.
- A track record of mentoring engineers and developing future technical leaders.
What You'll Love About NetDocuments
- The People!
- 90% healthcare premiums company covered
- HSA company contribution
- 401K match at 4% with immediate vesting
- Flexible PTO (typically 3 to 4 weeks a year)
- 10 paid holidays
- Monthly contributions for life activities \& wellness
- Access to LinkedIn learning with monthly dedicated time to explore
Compensation Transparency
The compensation range for this position is: $180,000 \- $200,000
*The posted cash compensation for this position includes on target earnings. Some roles may qualify for overtime pay. Individual compensation packages are determined based on various factors specific to each candidate, such as career level, skills, experience, geographic location, qualifications, and other job\-related considerations.*
Equal Opportunity
*NetDocuments is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. All employment decisions are based on business needs, job requirements, individual qualifications, without regard to race, color, religion, sex, (including pregnancy), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity and/or expression, military and veteran status, or any other status protected by laws or regulations in the locations where we operate. NetDocuments believes diversity and inclusion among our employees is critical to our success, and we are committed to providing a work environment free of discrimination and harassment.*
Salary Context
This $180K-$200K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At NetDocuments, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $180K to $200K.
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.
NetDocuments AI Hiring
NetDocuments has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Lehi, UT, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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