Principal Applied AI Developer, Foundation Models Infrastructure

Autodesk•Toronto, ON

5d•CA$153,000 - CA$224,400

About The Position

The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, products, infrastructure, and entertainment, we empower some of the most creative people in the world. Autodesk is building cloud-scale software, data platforms, and AI-enabled capabilities that help customers design, build, and operate the world around them. As a Principal Applied AI Developer on the Foundation Model ML Infrastructure team, you will help define and accelerate the roadmap for the Autodesk Machine Learning Platform, the platform used by Autodesk researchers, ML developers, and product teams to support the full lifecycle of Autodesk’s machine learning models. You will design, build, and evolve resilient, secure, scalable, observable, and cost-effective platform services that support model training, inference, evaluation, deployment, and serving at global scale. You will work closely with researchers, ML developers, product teams, security, privacy, and platform partners to translate ambiguous business and technical requirements into robust platform capabilities with excellent developer experience and strong self-service workflows. This is a principal-level technical leadership role for someone who combines deep hands-on engineering expertise with the ability to define technical direction, lead complex initiatives across teams, mentor senior developers, and raise the bar for engineering excellence.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Machine Learning, or equivalent practical experience
8+ years of professional software engineering experience, including significant experience with large-scale, cloud-native, distributed, platform, or machine learning infrastructure systems
Experience using AI-assisted development tools, coding agents, and AI-powered automation to improve engineering productivity, with practical understanding of context design, human review, testing, secure usage, and integration into developer workflows
Deep hands-on experience designing, building, and operating production-grade services that support model training, inference, serving, evaluation, deployment, or observability
Strong experience with Kubernetes and cloud-native infrastructure
Experience with distributed compute, ML infrastructure, or model serving technologies such as Ray, SageMaker, distributed training platforms, inference serving platforms, or equivalent systems
Proven ability to lead complex technical initiatives across teams and influence technical direction without direct authority
Experience translating ambiguous research, product, or business requirements into practical technical designs and executable engineering plans
Strong experience designing and operating resilient, secure, observable, and cost-effective production systems using CI/CD, automated testing, infrastructure as code, monitoring, alerting, and production operations practices
Demonstrated ability to mentor developers, elevate engineering standards, and act as a strong technical voice for excellence
Strong written and verbal communication skills, with the ability to influence technical and non-technical stakeholders

Nice To Haves

Experience with advanced AI-assisted development patterns such as harness engineering, coding-agent orchestration, skills, MCPs, prompt/context design, evaluation loops, and reusable AI-enabled engineering workflows
Experience building or evolving internal developer platforms, ML platforms, self-service infrastructure, or platform APIs used by researchers and ML developers
Experience supporting large-scale model training, fine-tuning, batch inference, real-time inference, model serving, or foundation model workflows
Experience defining platform architecture, technical strategy, service boundaries, reliability standards, production readiness criteria, and operational practices
Experience designing systems with clear SLAs or SLOs for latency, throughput, availability, reliability, and cost
Familiarity with model governance, model versioning, lineage, evaluation workflows, Trusted AI, security, privacy, and responsible AI practices
Track record of leading cross-team technical initiatives, raising engineering quality, and improving developer productivity
Experience working with AI research teams, ML developers, product teams, and platform organizations to productionize applied AI capabilities

Responsibilities

Define and drive technical strategy for Foundation Model ML Infrastructure capabilities within the Autodesk Machine Learning Platform
Lead the design and implementation of large-scale platform services that support the full lifecycle of Autodesk’s ML models, including training, inference, serving, evaluation, deployment, monitoring, and operations
Architect highly resilient, secure, observable, scalable, and cost-effective infrastructure for large-scale AI and ML workloads
Build and evolve developer-facing APIs, tools, workflows, and self-service capabilities that enable researchers and ML developers to move quickly and safely
Work hands-on with Kubernetes, Ray, SageMaker, AWS, and related cloud-native technologies to support distributed training, scalable inference, and production model serving
Identify, frame, and prioritize high-impact technical problems aligned with product, research, and platform strategy
Translate ambiguous AI research goals, product needs, and business requirements into practical technical designs and executable engineering plans
Lead complex cross-team technical initiatives, align stakeholders, and influence technical direction without requiring direct authority
Drive reliability, scalability, performance, security, quality, and cost improvements across training, inference, and serving workloads
Establish and evolve platform standards for production readiness, observability, SLAs/SLOs, incident response, release quality, model deployment, versioning, lineage, and governance
Partner with researchers, ML developers, product managers, architects, security, privacy, and platform teams to define quality bars and safe production deployment practices, including Trusted AI requirements
Improve developer productivity through CI/CD, automated testing, infrastructure as code, contract testing, quality gates, documentation, and platform automation
Lead root-cause analysis for systemic production issues and implement durable, platform-level improvements
Act as a technical authority for critical decisions, guiding trade-offs across performance, reliability, security, cost, scalability, and developer experience
Mentor senior developers, elevate engineering standards, and foster a culture of ownership, quality, action, and accountability
Actively participate in Agile, Kanban, or other modern development methodologies to deliver high-quality outcomes incrementally