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Head of Engineering, ML, Forma Generative AI

  • Toronto, ON, Canada

For Canada based roles, we expect a starting base salary between $156,000 and $228,800. · Full-time

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Job details

Pay
For Canada based roles, we expect a starting base salary between $156,000 and $228,800.
Job type
Full-time
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Posting language
English
Posted

Original job description

Job Requisition ID #

26WD101528

Position Overview

Autodesk is transforming how the AEC industry designs and builds through integration of Artificial Intelligence (AI) and Machine Learning (ML) across its products, including AutoCAD, Revit, and Forma.

As Head of Engineering, ML for Forma Generative AI, you will lead the ML Engineering organization responsible for computational design and data representations, applied ML model development, and ML infrastructure and operations. You will lead through three engineering managers, setting the ML strategy for the team, defining the technical and organizational standards across the discipline, and shaping how teams build, deploy, and operate production ML systems.

You will partner closely with Architecture, Software Engineering, Research, Product, and other senior leaders to define technical direction, resolve cross-organizational trade-offs, and ensure the organization can move from promising ML research to reliable product capabilities at scale.

Report: You will report to the Director, Forma Generative AI.

Location: We support hybrid work, and you work near our Toronto, Canada office. This role requires the ability to travel to team meetings, offsites, and conferences in the USA, Canada, and Europe.

Responsibilities

  • Define and uphold ML engineering strategy, standards, and expectations across the organization, including how ML systems are designed, tested, reviewed, deployed, evaluated, and operated in production
  • Lead and develop the engineering managers responsible for Computational Design & Data Representations, Applied ML, and ML Infrastructure and Operations. Own the evolution of the ML engineering organization, including team structure, boundaries, hiring, succession, and organizational design
  • Partner with Architecture and peer engineering leaders to establish shared ML architectures, reusable components, and engineering practices that enable teams to move quickly without compromising quality or consistency
  • Partner with Research, Product, and Engineering leaders to balance long-term ML research and platform investments with iterative product delivery; align priorities, staffing, and capacity across the ML organization; surface constraints early and resolve cross-team trade-offs to keep critical work moving
  • Represent ML engineering in organizational planning, resourcing, escalations, and major technical decisions, serving as the discipline’s leadership voice across Forma Generative AI
  • Build a high-trust engineering culture that develops strong technical leaders, encourages knowledge sharing, and raises the quality of engineering judgement across the organization

Minimum Qualifications

  • BS or MS in Computer Science, Machine Learning, or a related field, or equivalent experience
  • 8+ years of software/ML engineering experience, including 4+ years of engineering management experience and demonstrated experience managing engineering managers or leading multiple engineering teams
  • Track record building and operating production ML systems at scale across training, deployment, inference, monitoring, and evaluation
  • Experience establishing engineering standards and technical direction across multiple teams without centralizing every decision
  • Demonstrated success hiring, developing, and retaining engineering managers and senior technical leads, with strong communication and influence across senior stakeholders

Preferred Qualifications

  • Experience leading engineering organizations responsible for large-scale ML or foundation-model systems
  • Deep technical familiarity with modern ML engineering and production infrastructure, including ML frameworks (e.g. PyTorch), distributed training and inference, cloud infrastructure, observability, CI/CD, Infrastructure-as-Code, and production automation
  • Experience designing or leading development of foundation-model and agentic systems, including evaluation harnesses, tool-use/orchestration patterns, and other production scaffolding around models
  • Experience partnering with peer engineering, architecture, research, and product leaders in a matrixed organization
  • Experience in the AEC industry, Autodesk products, or another technically complex design/engineering domain
  • Experience applying rapidly evolving foundation-model and agentic-system technologies in production environments

The Ideal Candidate

  • You know the difference between managing a team and leading an organization; you develop strong managers, create clear ownership, and scale through others
  • You can establish a technical bar and make it stick through standards, coaching, and strong engineering judgement rather than personal gatekeeping
  • You build trust with senior peers across architecture, research, product, and engineering, and can navigate disagreement and ambiguity while keeping the organization moving
  • You communicate effectively across disciplines and levels, adapting your approach to build shared understanding with technical, product, research, and executive audiences

Learn More

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $156,000 and $228,800. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

Belonging

We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

In-Person Onboarding and Identity Verification

This role may require in-person onboarding and/or in-person ID verification.
More about this job

Responsibilities

Lead the ML engineering organization through three engineering managers, setting strategy and standards across computational design and data representations, applied ML, and ML infrastructure and operations. Shape organizational structure and technical direction, align research and product priorities, and build a high-trust culture that delivers reliable production ML systems at scale.

Requirements

Requires a bachelor’s or master’s degree in computer science, machine learning, or a related field, or equivalent experience, plus 8+ years of software or ML engineering experience and 4+ years of engineering management experience. Candidates should have experience leading managers or multiple teams, operating production ML systems at scale, setting standards across teams, and developing engineering leaders.

Skills

  • Machine Learning Engineering
  • Engineering Management
  • Organizational Design
  • Production Machine Learning Systems
  • Distributed Training And Inference
  • PyTorch
  • Cloud Infrastructure
  • Observability
  • CI/CD
  • Infrastructure-As-Code
  • Foundation Models
  • Agentic Systems
  • Model Evaluation
  • ML Infrastructure And Operations
  • Computational Design
  • Cross-Functional Leadership

Benefits

  • Annual Cash Bonuses
  • Stock Grants

Visa sponsorship

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Categories

  • Technology
  • Management & Leadership
  • Software
  • Engineering
  • Data & Analytics

Keywords

  • Artificial Intelligence
  • Machine Learning
  • ML Engineering
  • Computational Design
  • Data Representations
  • Applied ML
  • ML Infrastructure
  • Production ML Systems
  • Model Training
  • Model Deployment
  • Inference
  • Monitoring
  • Evaluation
  • PyTorch
  • Distributed Training
  • Cloud Infrastructure
  • Observability
  • CI/CD
  • Infrastructure-As-Code
  • Production Automation
  • Foundation Models
  • Agentic Systems
  • Evaluation Harnesses
  • Tool Use
  • Orchestration
  • AEC
  • AutoCAD
  • Revit
  • Forma
  • Organizational Design

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