Principal ML Engineer, Machine Learning Platform and Systems Architecture

Autodesk•Boston, MA

3d•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

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