Senior Principal Machine Learning Engineer

26WD94803

Senior Principal Machine Learning Engineer, ML Platform and Systems Architecture

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 seeking a Senior Principal ML Engineer, ML Platform and Systems Architecture to define and drive the technical strategy for large-scale machine learning platforms and systems. This is a top-level engineering leadership role for a technical authority who can shape multi-year architecture, influence engineering standards across teams, and lead major platform initiatives that connect research, product, and business goals. You will be responsible for driving the evolution of the systems that enable machine learning across Autodesk, including training infrastructure, data platforms, evaluation and experimentation systems, model serving frameworks, and operational excellence for production ML. You will work across organizational boundaries to guide decisions, resolve hard technical challenges, and ensure that platform investments are aligned with long-term product and business outcomes. This role is fully remote-friendly, with team members distributed across the US and Canada.

Location

US or Canada Remote

Responsibilities

• Define and lead technical strategy for a domain or large-scale platform supporting machine learning systems
• Drive architecture decisions across teams for scalable training, data, evaluation, deployment, observability, and reliability systems
• Lead multi-team initiatives with far-reaching technical impact across a function, platform, or division
• Define technical direction for data pipelines that support large-scale structured and semi-structured technical datasets
• Set standards for data lineage, provenance, governance, and responsible data usage in ML systems
• Lead architecture for distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms
• Define scalable approaches for model deployment, inference services, monitoring, and observability for production ML systems
• Influence platform direction for ML-ready representations of geometry, graph, hierarchical, or multimodal data
• Influence standards for engineering quality, architecture, resiliency, risk management, and operational excellence
• Identify long-term technical and operational risks and guide investment decisions that future-proof platform capabilities
• Serve as a technical authority and trusted advisor to engineering leaders, senior engineers, and cross-functional stakeholders
• Resolve complex cross-team technical problems by framing options, aligning stakeholders, and driving execution
• Champion engineering practices that improve service quality, release readiness, monitoring, incident response, and