Manager, Software Engineering - AI Assistant

NetDocuments•Lehi, UT

9d•$180,000 - $200,000•Hybrid

About The Position

NetDocuments is seeking an Engineering Manager to lead the AI Assistant engineering team through its next chapter. This role is crucial as the company is building the next generation of the Assistant's capabilities, focusing on intelligent, agentic AI that can take actions on a user's behalf, specialized agents for common legal tasks, and secure integrations. The work is live, with products shipping to real customers and the team growing. The Engineering Manager will play a key role in shaping the AI Assistant's future and developing leadership skills. The role is guided by principles of validating before scaling, being AI-forward, customer feedback-driven roadmapping, and prioritizing security and trust. NetDocuments is a hybrid, remote-friendly workplace.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

Lead and develop a high performing 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.
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.
Coordinate across multiple engineering teams contributing to a shared product vision.
Communicate priorities, risks, and progress clearly to technical and nontechnical stakeholders.
Build the partnerships that drive alignment and execution across the organization.
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.
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.