Machine Learning Engineer, ML Systems and Infrastructure
New
Canada; This role is fully remote-friendly, with team members distributed across the US and Canada.Full-TimeJunior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- At least 2 years
- Required Skills
- AWSPythonGCPAirflowAzureSparkCI/CDSoftware EngineeringDistributed Systems
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
- 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
View Full Description & ApplyYou'll be redirected to the employer's site
