Senior Machine Engineer, ML Systems and Infrastructure

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent industry experience
• At least 3 to 4 years of industry experience building and operating production software, ML systems, distributed infrastructure, or large-scale data pipelines
• Strong experience in software engineering, distributed systems, backend systems, or ML infrastructure
• Strong proficiency in Python and experience delivering production-quality systems
• Experience designing and operating scalable data or compute pipelines
• Experience with cloud platforms such as AWS, Azure, or GCP
• Familiarity with containers, CI/CD, observability, and release quality practices
• Ability to independently drive technical execution on complex work with limited oversight

PREFERRED QUALIFICATIONS

• Experience building data pipelines for large-scale structured and semi-structured technical datasets
• Experience with data lineage, provenance, governance, and responsible data usage in ML systems
• Experience with distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms
• Experience with model deployment, inference services, monitoring, and observability for production ML systems
• Experience building ML-ready representations for geometry, graph, hierarchical, or multimodal data
• Experience with distributed ML frameworks such as PyTorch, Lightning, DeepSpeed, FSDP, Megatron, or similar
• Familiarity with AEC workflows, design data, BIM/CAD formats, or Autodesk products

What You'll Be Doing

• Design and build scalable systems for ML training, evaluation, deployment, and monitoring
• Develop and improve data pipelines that process large-scale structured and semi-structured technical datasets
• Optimize distributed workflows for performance, reliability, resource utilization, and cost efficiency
• Build platform capabilities such as experiment tracking, model versioning, checkpointing, reproducibility, and observability
• Contribute to model deployment, inference services, and production monitoring workflows
• Improve data quality, lineage, provenance, and operational transparency across ML pipelines
• Contribute to architecture and design discussions across the team
• Identify and resolve bottlenecks in data, compute, orchestration, and observability layers
• Mentor engineers through code reviews, design guidance, and knowledge sharing
• Collaborate closely with researchers, product engineers, and platform partners to turn ML workflows into robust engineering systems

Perks and Benefits

• This role is fully remote-friendly
• Team members are distributed across the US and Canada