Autodesk
PrincipalMLEngineer,MachineLearningPlatformandSystemsArchitecture
Neural analysis suggests this role is
optimal for Principal candidates.
“Principal ML Engineer, Machine Learning Platform and Systems Architecture at Autodesk. Skills: ML Platform, Systems Architecture, Distributed Computing, End-to-end Platform. Lead architecture and delivery for major ML platform. Design scalable systems for distributed training”
Industry & Context.
Clarify ambiguous problem spaces; Define solution approaches
What They're Looking For.
Must Have
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, or equivalent industry experience, 6 to 8 years of industry experience in software engineering, ML infrastructure, distributed systems, or platform engineering, Experience leading design and delivery of complex technical systems, Deep experience in software architecture, distributed systems, large-scale data platforms, or ML infrastructure, Proficiency in Python, Command of production software engineering practices, Experience leading complex technical initiatives with multiple engineers or cross-functional teams, Experience with large-scale data pipelines, Experience with distributed data processing, Experience with cloud-native platform architectures, Experience with model deployment, Experience with inference systems, Experience with production observability, Demonstrated ability to make architecture decisions balancing performance, scalability, reliability, and cost, Communication and stakeholder management skills
Nice to Have
Experience building data governance, lineage, and provenance capabilities for ML platforms, Experience building ML-ready representations for geometry, graph, hierarchical, or multimodal data, Deep experience with distributed ML frameworks and large-scale training infrastructure, Experience with Kubernetes, workflow orchestration systems, and modern ML platform tooling, Experience with production incident leadership, service reviews, resiliency practices, and operational readiness, Familiarity with AEC data, computational design workflows, BIM/CAD ecosystems, or Autodesk products
What You'll Do.
Lead architecture and delivery for major ML platform
Design scalable systems for distributed training
Design scalable systems for data processing
Design scalable systems for feature lifecycle management
Design scalable systems for model lifecycle management
Design scalable systems for production inference
Own platform-level technical outcomes from design through deployment
Own platform-level technical outcomes through operations
Own platform-level technical outcomes through continuous improvement
Drive the design and scaling of data pipelines
Drive the design and scaling of data pipelines
Lead architecture for distributed data processing systems
Lead architecture for orchestration systems
Establish practices for data lineage
Establish practices for data provenance
Establish practices for data governance
Establish practices for responsible data usage in ML
Guide the design of model deployment
Guide the design of inference services
Guide the design of monitoring for production ML
Guide the design of observability for production ML
Contribute to the development of ML-ready representations for
Contribute to the development of ML-ready representations for
Contribute to the development of ML-ready representations for
Contribute to the development of ML-ready representations for
Clarify ambiguous problem spaces
Define solution approaches
Lead execution across multiple engineers and teams
Establish engineering standards for ML systems
Improve engineering standards for ML systems
Establish operational practices for ML systems
Improve operational practices for ML systems
Establish architectural patterns for ML systems
Improve architectural patterns for ML systems
Lead incident response for critical platform issues
Drive lasting improvements across system health
Drive lasting improvements across system supportability
Act as a force multiplier through design leadership
Act as a force multiplier through coaching
Act as a force multiplier through technical reviews
Communicate technical strategy to stakeholders
Communicate tradeoffs to stakeholders
Communicate execution plans to stakeholders
How You'll Work.
Team & Collaboration
Cross-functional teams; Multiple engineers
Communication Scope
Stakeholder management; Technical strategy; Tradeoffs; Execution plans
Full Job Description
**Job Requisition ID #** 26WD97132 **26WD97132,**Pr** incipal Machine Learning Engineer, ML Platform and Systems Architecture** _French translation to follow!/Traduction française à suivre!_ **Position Overview** The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings,machines, and even the latest movies, we influence and empower some of the most creative people in the world to solve problems that matter. Autodesk is looking for a **Principal ML Engineer, ML Platform and Systems Architecture** to lead the design and evolution of large-scale machine learning platforms. In this role, you will own high-impact technical initiatives that span ML infrastructure,data systems, model lifecycle tooling, and production architecture. You will work closely with researchers, product teams, andengineering leadership to build the systems that bring advanced machine learning into reliable, scalable product experiences.This is a senior technical leadership role for an engineer who excels at system architecture, distributed computing, and end-to-end platform thinking. You will help define the technical direction for ML systems and drive execution across ambiguous, cross-functional, high-value initiatives.This role is fully remote-friendly, with team members distributed across the US and Canada. **Location:** US or Canada Remote **Responsibilities** * Lead architecture and delivery for major ML platform capabilities across training, evaluation, deployment, and observability * Design scalable systems for distributed training, data processing, feature and model lifecycle management, and production inference * Own platform-level technical outcomes from design through deployment, operations, and continuous improvement * Drive the design and scaling of data pipelines for large-scale structured and semi-structured technical datasets * Lead architecture for distributed data processing and orchestration systems such as Ray, Airflow,
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