Autodesk
Senior Machine Learning Engineer
Toronto, ON, CAN · Senior
Sponsorship not specifiedDetected 13 days ago
PythonCode ReviewGitSQLSnowflakeAWSCloud PlatformsPlatform EngineeringMachine LearningAirflowdbtData EngineeringData ScienceAgentic AILLMOpsMLOpsAI OrchestrationA/B TestingAgileManual TestingCommunicationProblem Solving
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odds of building a lasting career here
38Risky
Cap-exempt (no lottery)0
Sponsors this role85
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
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from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 25 green-card filings in this occupation, 100% were for a worker who already held the job.
Green-card follow-through: 86%
Of 29 labor certifications old enough to have been used, 4 expired without the employer filing the next step. Median time from filing to decision: 483 days.100% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.
Reported a layoff in this occupation
In a green-card filing decided 2026-02-13, this employer stated it had laid off workers in Software Developers (CA) during the preceding six months.Describes the period before that filing, not necessarily today. Unlike WARN notices this is occupation-specific and federal.
Files H-1B transfers
12 transfer filings in the last year, covering 12 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- Amazing things are created every day with our software - from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies.
- We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.
- Ready to shape the world and your future?
Responsibilities
- Design, develop, test, deploy, and maintain production-grade ML pipelines supporting enterprise-scale use cases
- Develop reusable ML services and components
- Develop robust model evaluation frameworks covering dimensions such as accuracy, relevance, groundedness, consistency, latency, throughput, robustness, and cost
- Design automated evaluation pipelines using deterministic metrics, model-based evaluation, curated datasets, regression testing, and human evaluation where appropriate
- Build and maintain distributed processing pipelines capable of handling large volumes of documents, web content, structured data, and unstructured data efficiently
- Design and optimize distributed pipeline and cloud orchestration for large-scale AI workloads using appropriate workflow orchestration and cloud-native technologies
- Implement resilient processing patterns including concurrency management, queue-based architectures, checkpointing, retries, failure recovery, rate limiting, and idempotent processing
- Optimize AI/ML systems for latency, throughput, scalability infrastructure utilization, and model inference cost
- Partner with platform engineering teams to integrate AI applications with the relevant platforms, APIs, identity and access management, monitoring, and deployment infrastructure
- Implement appropriate MLOps and LLMOps practices, including model and prompt versioning, experiment tracking, evaluation, deployment automation, monitoring, rollback mechanisms, and lifecycle management
Requirements
- Demonstrated experience designing and operating production ML systems rather than only experimentation or notebook-based model development
- Working experience with Snowflake, Hands-on experience with Snowflake utilities, Snow SQL, Snow Pipe.
- Must have worked on Snowflake Cost optimization scenarios
- Experience with workflow orchestration technologies such as Airflow or comparable orchestration frameworks
- Experience designing distributed data or ML processing pipelines for high-volume workloads
- Experience deploying workloads into a major cloud environment, preferably AWS, and working with cloud services for compute, storage, event processing, monitoring, and distributed execution
- Experience with Git-based software development workflows, code reviews, branching strategies, and collaborative engineering practices
- Familiarity with MLOps concepts, including experiment tracking, model lifecycle management, deployment, model monitoring, reproducibility, and versioning
- Experience working with structured and unstructured data and designing preprocessing, enrichment, and transformation pipelines.
- Strong analytical, debugging, and problem-solving skills with the ability to diagnose issues across application, model, pipeline, and infrastructure layers
Compensation
- Salary transparency
Benefits
- Build comprehensive observability and monitoring mechanisms across ML pipelines, covering pipeline health, model performance, data quality, failures, and cost
Company info
- We take great pride in our culture here at Autodesk - it's at the core of everything we do.
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This listing is sourced directly from Autodesk's careers page and normalized into a canonical job model.