Director of Engineering, AI-Native R&D
About Nerdio
At Nerdio, our mission is to simplify the lives of IT professionals and maximize their Microsoft cloud and end user computing investments. We support organizations of all sizes looking to deploy, manage, and cost-optimize native Microsoft technologies. We partner with Enterprises and Managed Service Providers all over the world to add value on top of their existing native Microsoft investments like Azure Virtual Desktop (AVD), Windows 365, and Microsoft Intune.
Created in 2016, Nerdio has always taken a market-leading and collaborative approach to cloud deployment and management. In fact, our product roadmap is greatly influenced by the regular feedback we receive from having seen companies deploy AVD into production environments several thousand times using Nerdio technology. Today, Nerdio is used in over 50 countries by more than 15,000 organizations of every size and vertical.
We’re committed to delivering exceptional service and support, which starts with identifying and supporting the best staff possible. We are a fast-moving, nimble company looking for individuals who are collaborative, empathetic, driven and who love to move at the speed of light. If you want to be part of the AVD transformation that Microsoft and Nerdio are leading, then we want to speak with you.
About the Role
We are looking for a Director of Engineering (R&D) to lead and scale Nerdio's AI-native R&D organization, currently 10+ engineers, with accountability for its strategy, structure, and outcomes. This role combines organizational leadership with selective hands-on engineering. You will stay close enough to the work to set the technical bar and earn trust on the group's hardest engineering problems, and you will establish responsible AI-assisted development practices across the organization.
You will own the technical execution of complex problems from initial concept through implementation, evaluation, product validation, and production readiness. You will use AI coding agents and agentic frameworks as part of your daily workflow and help the team do the same without sacrificing code quality, security, reliability, or architectural discipline. You will turn ambiguous needs into clear, testable specifications, measurable evaluation criteria, and executable engineering backlogs, then ensure validated concepts are production-ready or transferred to product engineering through clear documentation and structured handoffs.
What You'll Do
People Leadership
- Lead and mentor the team, fostering a culture of ownership, accountability, experimentation, and continuous improvement.
- Run effective 1:1s, performance reviews, and career development conversations that help each engineer grow, thrive, and understand what success looks like.
- Set clear expectations, provide timely feedback, recognize strong contributions, and address performance or team-health concerns directly and constructively.
- Hire, onboard, and develop engineers who can operate effectively in a fast-moving environment where tools, practices, and technical assumptions evolve quickly.
- Create an inclusive, psychologically safe team environment where engineers can challenge ideas, learn from failed experiments, and make decisions with appropriate speed and rigor.
- Balance the demands of people leadership and hands-on engineering, protecting focus while staying technically credible and accessible to the team.
Technical Leadership and Hands-On Engineering
- Own R&D initiatives end to end. Take ideas through specification, technical design, implementation, testing, evaluation, product validation, documentation, production readiness, and a structured handoff to product engineering with a high degree of independence and accountability.
- Build with AI in the loop. Use AI coding assistants, LLM APIs, coding agents, and agentic frameworks such as Microsoft Agent Framework or comparable orchestration technologies to accelerate coding, review, testing, and debugging, while exercising sound judgment about when not to rely on them. End-to-end SDLC experience using AI Factory and AI harness processes.
- Turn vague needs into executable specifications. Work with stakeholders to define scope, assumptions, technical constraints, acceptance criteria, evaluation measures, and an actionable engineering backlog. Iterate through short feedback loops as evidence and requirements evolve.
- Develop the team’s AI engineering capabilities. Mentor engineers in writing precise specifications and prompts, reviewing generated code critically, designing tests and evaluations, and identifying hallucinations, security risks, and architectural drift.
- Set and uphold a high engineering bar. Establish and enforce practical standards for architecture, clean code, code review, automated testing, secure development, documentation, and production readiness.
- Make sound architectural decisions. Guide system design across distributed services, APIs, data flows, agent orchestration, and cloud infrastructure, balancing rapid experimentation with maintainability and scale.
- Measure and improve the workflow. Create feedback loops that evaluate both software outcomes and the effectiveness of AI-assisted engineering practices, then turn lessons into repeatable team standards.
- Partner across functions. Work with Product Management, Architecture, Security, and engineering stakeholders to identify high-value R&D opportunities, define technical and product validation criteria, and determine when validated concepts are ready for productionization or transfer to a product team.
Required Qualifications
- 7+ years of professional software engineering experience, including ownership of complex systems from design through production.
- 4+ years of engineering management experience with direct responsibility for coaching, performance management, hiring, career development, and team health.
- Demonstrated success leading an engineering team of comparable size while remaining hands-on with prototypes, incubation code, architecture, critical technical reviews, and the productionization of validated concepts.
- Strong software engineering fundamentals across backend development, distributed systems, system design, API design, testing, and clean code.
- Hands-on experience using AI coding assistants, coding agents, and agentic workflows in real software delivery work, not only demonstrations or personal experiments.
- Strong proficiency in C#/.NET, or comparable backend expertise with the ability to become productive quickly in C#, plus experience integrating LLM APIs into applications or engineering workflows.
- Experience with a major cloud platform such as Microsoft Azure, AWS, or Google Cloud, containerized development with Docker, and container orchestration technologies such as Kubernetes.
- Practical knowledge of observability, including structured logging, metrics, tracing, monitoring, alerting, and evaluation of AI-powered features.
- Ability to review AI-generated code critically for correctness, security, maintainability, testing gaps, and architectural fit.
- Working knowledge of responsible AI-assisted engineering practices, including source-code and data-handling boundaries, human review, testing, traceability, and secure use of approved tools.
- Excellent written and verbal communication, sound technical judgment, and comfort turning ambiguity into decisions and executable plans.
Preferred Qualifications
- Experience building or leading an R&D, incubation, platform, or advanced engineering team working through high levels of uncertainty.
- Experience building products or leading engineering teams in the Microsoft cloud, virtualization, or end-user computing ecosystem, including Azure, Azure Virtual Desktop, Windows 365, Microsoft Intune, or comparable technologies.
- Experience designing evaluations for LLM or agent behavior, including quality, reliability, regression, safety, cost, and latency considerations.
- Experience with multi-tenant SaaS architectures and their security, data isolation, performance, and operational challenges.
- Background in DevOps practices, CI/CD pipelines, and infrastructure as code.
- Experience establishing AI-assisted engineering standards or governance across a team or organization.
- A record of improving engineering culture through technical mentorship, onboarding, internal tooling, technical talks, or documented engineering standards.
What Success Looks Like
- The R&D organization has a clear strategy and roadmap, a healthy leadership bench, and a trusted reputation with product, architecture, and executive partners.
- The organization converts ambiguous ideas into precise specifications, rapid experiments, measurable evaluations, and validated product opportunities.
- Engineers use AI-assisted workflows effectively while retaining human accountability for architecture, security, testing, and code quality.
- Validated concepts are transferred to product engineering through clear documentation and structured handoffs.
- Leaders and engineers across the team are growing, and the team can scale without quality or culture eroding.
- The team demonstrates clear ownership, candid feedback, sustainable execution, meaningful career growth, and consistently high engineering standards.
Benefits and Incentives
- Competitive Base and Incentive Plan
- Stock Options
- Health and Welfare Plans*
- Life and Disability Plans*
- Retirement Plan*
- Unlimited Flexible Paid Time Off, including your birthday off!
- Collaborative Team Culture
* Benefits for international employees, outside the US, vary by country.
Equal Opportunity Statement
Nerdio is committed to a diverse and inclusive workplace. Nerdio is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Location & Pay
The pay range for this role is: 145,000 - 165,000 USD per year (United Kingdom)
