Machine Learning Engineer, ML Systems and Infrastructure

Autodesk•Boston, ON

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 an ML Engineer, ML Systems and Infrastructure to help build the technical foundation behind large-scale machine learning systems. In this role, you will partner with AI researchers, software engineers, and platform teams to build scalable pipelines, training infrastructure, data workflows, and production-ready ML systems that support the next generation of AI-powered product experiences. This is an engineering-first role focused on building and operating ML systems at scale. You will work on problems such as distributed training workflows, data processing pipelines, model evaluation infrastructure, deployment systems, and platform tooling that improves reliability, efficiency, and developer velocity. 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
At least 2 years of industry experience in software engineering, machine learning infrastructure, distributed systems, data platforms, or related areas
Strong software engineering fundamentals, including coding, testing, debugging, and code quality
Proficiency in Python and experience building production-quality software
Experience with cloud platforms such as AWS, Azure, or GCP
Familiarity with containers, version control, CI/CD, and modern development workflows
Experience working with data-intensive systems, backend systems, or ML pipelines
Ability to work independently on well-defined problems with moderate ambiguity

Nice To Haves

Experience building data pipelines for large-scale structured and semi-structured technical datasets
Familiarity with data lineage, provenance, governance, and responsible data usage in ML systems
Familiarity with distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms
Familiarity with model deployment, inference services, monitoring, and observability for production ML systems
Familiarity with ML-ready representations for geometry, graph, hierarchical, or multimodal data
Experience working with CAD, BIM, AEC, or other complex domain-specific data formats

Responsibilities

Build and maintain components of ML pipelines for data preparation, model training, evaluation, deployment, and monitoring
Develop reliable software and infrastructure that supports scalable machine learning workflows
Contribute to distributed data processing and training systems used by researchers and engineering teams
Support data ingestion, transformation, validation, and serving for large-scale structured and semi-structured technical datasets
Improve automation, testing, CI/CD, observability, and operational reliability for ML systems
Troubleshoot data, infrastructure, and performance issues in collaboration with senior engineers
Participate in design discussions and contribute ideas that improve system scalability, maintainability, and efficiency
Document technical decisions, workflows, and operational processes clearly