Manager, Applied AI/ML, Data Science & Engineering

Autodesk•AMER - Canada - British Columbia - Offsite/Home, BC

1d•CA$158,000 - CA$231,000•Remote

About The Position

We are hiring an engineering manager to lead a multidisciplinary team working across applied AI/ML engineering, data science, infrastructure, and production data systems. This manager will directly support senior and principal-level individual contributors, including Applied AI/ML Engineers and Data Scientists, and will be responsible for helping the team execute effectively in a fast-changing technical environment. This is not a traditional people-management role where the operating model is already fully defined. Engineering processes, delivery norms, quality expectations, AI/ML evaluation practices, and team workflows are all evolving rapidly as applied AI changes how software and data products are built. The right person will be energized by that uncertainty. They will bring structure without creating bureaucracy, help the team adapt quickly, and create an environment where strong technical generalists can do their best work. We need this person to have a high level of curiosity and flexibility, able to introduce and advocate for new ways of using AI tools in software development and delivery. This person will manage and work with teams operating across Canada, India, Europe, and North America. They must be highly effective in distributed, asynchronous collaboration and able to build trust, clarity, and momentum across time zones. Applied AI and data science teams are operating in a period of major change. The tools, processes, quality standards, and delivery expectations that worked for traditional software or analytics work may not be sufficient for modern AI-enabled systems. This role is critical because the team needs a manager who can help define new ways of working while still delivering meaningful outcomes. The right manager will help the team move through uncertainty with confidence: creating enough structure to make progress, enough flexibility to adapt, and enough curiosity to keep learning as the field changes.

Requirements

Experience managing technical teams in engineering, applied AI/ML, data science, data platforms, or adjacent domains
Ability to lead senior and principal-level ICs without needing to be the deepest expert in every area
Strong understanding of modern software, data, and AI/ML delivery practices, with enough technical depth to ask good questions, identify risks, and facilitate sound decisions
Comfort operating in environments where processes are still forming, changing, or being actively redefined
High adaptability and curiosity about how AI/ML is changing engineering practice, team structure, delivery models, and quality expectations
Strong cross-functional leadership skills, especially in ambiguous initiatives involving engineering, data science, infrastructure, QA, product, and business stakeholders
Excellent written communication, including planning docs, status updates, decision summaries, stakeholder updates, and async team communication
Excellent verbal communication, including facilitation, coaching, conflict resolution, and executive or cross-functional updates
Experience working with globally distributed teams, especially across India, Europe, and North America
Strong project and execution management skills, including planning, dependency tracking, prioritization, and risk management
A generalist mindset and willingness to engage across areas such as DevOps, AWS/cloud operations, data systems, infrastructure, quality, and delivery planning
Demonstrated ability to create team focus and accountability without over-prescribing solutions or slowing down strong ICs

Nice To Haves

The team has clear priorities, strong execution habits, and a shared understanding of what matters most
Senior and principal ICs feel supported, challenged, and empowered rather than micromanaged
Distributed collaboration improves across Canada, India, Europe, and North America
The team adapts quickly as applied AI engineering practices evolve. Delivery becomes more predictable without reducing experimentation, curiosity, or technical ambition
Engineering quality, communication, and operational ownership improve over time
The manager becomes a trusted partner to both technical ICs and cross-functional stakeholders

Responsibilities

Manage and grow a team of senior and principal-level engineers and data scientists working on applied AI/ML-enabled systems, data products, and platform capabilities
Create clarity in ambiguous technical and organizational environments by helping the team define priorities, execution plans, decision points, and success criteria
Build operating rhythms that work across India, Europe, and North America, including effective async communication, meeting discipline, handoff practices, and documentation norms
Partner closely with senior technical ICs to translate strategy and ambiguous opportunities into scoped initiatives, milestones, and measurable outcomes
Help the team navigate rapidly changing AI/ML engineering practices, including evolving norms around prototyping, evaluation, production readiness, quality, governance, and operational ownership
Drive continuous improvement in team processes without assuming that legacy engineering models are always the right fit for AI-driven work
Support cross-functional execution across engineering, data science, product, analytics, infrastructure, quality, and business stakeholders
Coach team members on communication, prioritization, technical judgment, stakeholder management, and working effectively across distributed teams
Identify risks, dependencies, bottlenecks, and unclear ownership early, then help the team resolve them pragmatically
Foster a team culture grounded in curiosity, adaptability, technical rigor, accountability, and psychological safety
Balance delivery pressure with sustainable team health, ensuring the team can move quickly without losing quality or focus
Recruit, onboard, and develop strong generalist technical talent capable of working across AI/ML, infrastructure, data systems, quality, and execution