Principal Machine Learning Engineer, Autodesk

• 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
• 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

Desirable

• 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
• 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