Principal ML Engineer, Machine Learning Platform and Systems Architecture

Principal Machine Learning Engineer, ML Platform and Systems Architecture

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, and engineering 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, Spark, or similar platforms
• Establish strong practices for data lineage, provenance, governance, and responsible data usage in ML systems
• Guide the design of model deployment, inference services, monitoring, and observability for production ML workloads
• Contribute to the development of ML-ready representations for geometry, graph, hierarchical, or multimodal data
• Clarify ambiguous problem spaces, define solution approaches, and lead execution across multiple engineers and teams
• Establish and improve engineering standards, operational practices, and architectural patterns for ML systems
• Lead incident response for critical platform issues and drive lasting improvements across system health and supportability
• Mentor engineers and act as a force multiplier through design leadership, coaching, and technical reviews
• Communicate technical strategy, tradeoffs, and execution plans clearly to technical and non-technical stakeholders

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent industry experience
• Typically 6 to 8 years of industry experience in software engineering, ML infrastructure, distributed systems, or platform engineering, including experience leading design and delivery of complex technical systems
• Deep experience in software architecture, distributed systems, large-scale data platforms, or ML infrastructure
• Strong proficiency in Python and strong command of production software engineering practices
• Experience leading complex technical initiatives that span multiple engineers or cross-functional teams
• Strong experience with large-scale data pipelines, distributed data processing, and cloud-native platform architectures
• Experience with model deployment, inference systems, and production observability
• Demonstrated ability to make architecture decisions that balance performance, scalability, reliability, and cost
• Strong communication and stakeholder management skills

Preferred Qualifications

• 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

The Ideal Candidate

• Is a strong architect and hands-on engineer
• Drives clarity and momentum in ambiguous spaces
• Thinks at platform level and acts with strong product and business awareness
• Raises the engineering bar for system design, quality, and operational excellence
• Builds trust through technical depth, calm judgment, and execution leadership