Principal Applied AI Developer, Foundation Models Infrastructure

Job Requisition ID #

26WD99600

English posting follows.

26WD99600, Senior Applied AI Developer, Core Model Infrastructure

Job Overview

The work we do at Autodesk touches virtually everyone on the planet. By creating software tools for designing buildings, machines, products, infrastructure and entertainment, we empower some of the world's most creative people.

Autodesk develops cloud-scale software, data platforms and AI-driven capabilities that help our customers design, build and operate the world around them. As a Senior Applied AI Developer on the Machine Learning Core Models Infrastructure team, you will help define and accelerate the roadmap for Autodesk's machine learning platform, used by Autodesk machine learning researchers, developers, and product teams to support the entire lifecycle of Autodesk's machine learning models.

You will design, develop and evolve resilient, secure, scalable, observable and cost-effective platform services that support training, inference, evaluation, deployment and provisioning of models globally. You will work closely with researchers, machine learning developers, product teams, security and privacy teams, and platform partners to translate ambiguous business and technical requirements into robust platform features, delivering an excellent developer experience and powerful self-service workflows.

This is a high-level technical leadership position for an individual combining deep practical engineering expertise with the ability to set technical direction, lead complex, multi-team initiatives, mentor senior developers, and raise the bar of engineering excellence.
Responsibilities

Define and drive the technical strategy for core model machine learning infrastructure capabilities within the Autodesk Machine Learning Platform

Lead the design and implementation of large-scale platform services supporting the entire lifecycle of Autodesk machine learning models, including training, inference, provisioning, evaluation, deployment, monitoring and operations

Design highly resilient, secure, observable, scalable, and cost-effective infrastructure for large-scale AI and machine learning workloads

Create and evolve developer APIs, tools, workflows, and self-service capabilities, enabling machine learning researchers and developers to move forward quickly and securely

Work hands-on with Kubernetes, Ray, SageMaker, AWS, and related cloud-native technologies to support distributed training, scalable inference, and production model provisioning

Identify, frame and prioritize high-impact technical issues, aligned with product, research and platform strategy

Translate ambiguous AI research goals, product needs, and business requirements into concrete technical designs and actionable engineering plans

Lead complex technical initiatives involving multiple teams, coordinate stakeholders, and influence technical direction without exercising direct authority

Drive improvements in reliability, scalability, performance, security, quality and cost control across training, inference and provisioning workloads

Establish and evolve platform standards for production readiness, observability, SLA/SLO, incident response, release quality, model deployment, release management, traceability and governance

Collaborate with researchers, machine learning developers, product managers, architects, and security, privacy, and platform teams to define quality criteria and secure production deployment practices, including requirements for trusted AI

Improve developer productivity with CI/CD, automated testing, infrastructure as code, contract testing, quality checks, documentation, and platform automation

Lead root cause analysis of systemic issues in production and implement sustainable improvements at the platform level

Act as the technical authority for critical decisions, guiding trade-offs between performance, reliability, security, cost, scalability and developer experience

Mentor senior developers, raise engineering standards and foster a culture of ownership, quality, action and responsibility

Actively participate in Agile, Kanban or other modern development methodologies in order to progressively deliver high quality results

Minimum Qualifications

Bachelor's or master's degree in computer science, computer engineering, machine learning, or equivalent practical experience

At least 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 technical productivity, with a practical understanding of contextual design, human review, testing, secure usage, and integration into developer workflows

Strong hands-on experience designing, building, and operating production-level services supporting model training, inference, provisioning, evaluation, deployment, or observability

Strong experience with Kubernetes and cloud-native infrastructures

Experience with distributed computing, machine learning frameworks or model delivery technologies such as Ray, SageMaker, distributed training platforms, inference delivery platforms or equivalent systems

Proven ability to lead
complex technical initiatives involving multiple teams and influencing technical direction without direct hierarchical authority

Experience in translating ambiguous research, product, or business requirements into concrete technical designs and actionable engineering plans

Strong experience in designing and operating resilient, secure, observable and cost-effective production systems using CI/CD practices, automated testing, infrastructure as code, monitoring, alerting and production operations

Proven ability to mentor developers, raise engineering standards, and be a strong technical voice for excellence

Strong written and oral communication skills, with the ability to influence technical and non-technical stakeholders

Desired Qualifications

Experience with advanced AI-assisted development models such as harness engineering, coding agent orchestration, skills, MCPs, prompt/context design, evaluation loops, and AI-based reusable engineering workflows

Experience building or scaling internal developer platforms, machine learning platforms, self-service frameworks, or platform APIs used by machine learning researchers and developers

Experience supporting large-scale model training, fine-tuning, batch inference, real-time inference, model provisioning, or basic model workflows

Experience in defining platform architecture, technical strategy, service boundaries, reliability standards, production maturity criteria and operational practices

Experience designing systems with clear SLAs or SLOs for latency, throughput, availability, reliability and cost

Knowledge of model governance, model versioning, traceability, evaluation workflows, trusted AI, security, privacy, and responsible AI practices

Proven experience leading cross-team technical initiatives, improving engineering quality, and optimizing developer productivity

Experience collaborating with AI research teams, machine learning developers, product teams, and platform organizations to bring applied AI capabilities into production

The Ideal Candidate

You are a highly experienced developer who combines deep technical expertise with the ability to set direction, foster cohesion and make an impact across different teams. You are comfortable in an uncertain environment, know how to identify the right technical problems and lead complex initiatives, from design to production

You think in terms of systems, platforms and developer experience, not just in terms of individual services or features. You know how to design scalable, secure, reliable, and cost-effective infrastructure that enables machine learning researchers and developers to build, train, deploy, and operate machine learning models securely and at scale

You have a strong bias for action and quickly make well-considered technical compromises. You find the right balance between short-term deliveries and the long-term health of the platform. You communicate clearly, build trust with your partners and help teams make better technical decisions

You are pragmatic, curious, collaborative, and passionate about solving complex infrastructure problems serving Autodesk researchers, machine learning developers, product teams, and global customers

26WD99600, Principal Applied AI Developer, Foundation Models Infrastructure

Position Overview

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.
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

Minimum Qualifications

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

Preferred Qualifications

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

The Ideal Candidate

You are a highly experienced developer who combines deep technical expertise with the ability to define direction, drive alignment, and deliver impact across teams. You are comfortable operating in ambiguity, framing the right technical problems, and leading complex initiatives from concept to production

You think in terms of systems, platforms, and developer experience, not just individual services or features. You understand how to design scalable, secure, reliable, and cost-effective infrastructure that enables researchers and ML developers to build, train, deploy, and operate machine learning models safely at scale

You have a strong bias toward action and make thoughtful technical tradeoffs quickly. You balance short-term delivery with long-term platform health. You communicate clearly, build trust with partners, and help teams make better technical decisions

You are pragmatic, curious, collaborative, and passionate about solving complex infrastructure problems in service of Autodesk ’s researchers, ML developers, product teams, and global customers

Plus d'information/ Learn More

À propos d’ Autodesk / About Autodesk

Bienvenue chez Autodesk ! Nos logiciels créent chaque jour des choses extraordinaires : des bâtiments les plus écologiques aux voitures les plus propres, en passant par les usines les plus intelligentes et les films à succès. Nous aidons les innovateurs à concrétiser leurs idées, transformant non seulement la façon dont les choses sont fabriquées, mais aussi ce qui peut l’être. Nous sommes très fiers de notre culture chez Autodesk ; elle est au cœur de tout ce que nous faisons. Notre culture guide notre façon de travailler et de nous comporter les uns envers les autres, influence nos interactions avec nos clients et nos partenaires, et définit notre image au monde. En tant qu’ Autodesk ien, vous pouvez accomplir un travail significatif qui contribue à bâtir un monde meilleur, conçu et réalisé pour tous. Prêt à façonner le monde et votre avenir ? Rejoignez-nous !

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 Autodesk er, 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!

Transparence salariale / Salary transparency
Le salaire est l'un des éléments du programme de rémunération concurrentiel d' Autodesk . Pour les postes basés au Canada, nous offrons un salaire de base entre $153,000 et $224,400. Le salaire est déterminé selon l'expérience professionnelle et l'emplacement du candidat(e). En plus du salaire de base, notre programme de rémunération peut inclure une prime annuelle, des commissions pour les postes de ventes, des attributions d'actions et un ensemble complet d'avantages sociaux.Salary is one part of Autodesk ’s competitive compensation package. For Canada based roles, we expect a starting base salary between $153,000 and $224,400. 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.
appurtenance / Belonging

Nous sommes fiers de cultiver une culture d’appartenance où chacun peut s’épanouir. Pour en savoir plus, cliquez ici:

We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here:

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Are you an existing contractor or consultant with Autodesk ?
Please search for vacancies and apply internally (not on this external site).

Please search for open jobs and apply internally (not on this external site).

Originally posted on Himalayas

Salary: CAD 153,000 – 224,400 (annual)