Sr. Engineering Manager – Data & AI/ML
| Company | Autodesk |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior, Expert or higher |
Requirements
- BSc or MSc in Computer Science, Computer Engineering, or equivalent industry experience.
- 8+ years of experience managing software development, data engineering, and/or ML teams.
- Proficiency in SQL optimization, performance tuning, and programming languages like Python, PySpark, Scala, etc.
- Experience with training and deploying PyTorch codebases.
- Proven track record of deploying production ML models.
- Expertise in ETL pipelines and orchestration tools like Airflow.
- Experience delivering production applications with AWS or similar platforms.
- Familiarity with modern software practices including CI/CD, Infrastructure-as-Code, version control, unit tests, and code documentation.
- Ability to document progress and provide proactive updates to partners and leadership.
- Skill in managing, influencing, and resolving complex and ambiguous issues.
Responsibilities
- Build and lead a team of highly talented ML engineers.
- Manage a team of data engineers and analysts to ensure efficient operations and robust design solutions.
- Implement best practices for the software development life cycle, system integrations, security, performance, and data management.
- Drive end-to-end data and ML workflows for domain-specific use cases.
- Collaborate with Product Management and stakeholders to understand requirements, evaluate new features, and develop technology roadmaps.
- Partner with architects and technical leads to create impactful solutions.
- Build an inclusive culture, mentor team members, and empower teams to achieve their full potential.
- Foster a culture of innovation, continuous improvement, and excellence in software development, Machine Learning, and data engineering.
Preferred Qualifications
- Hands-on expertise in ETL pipelines, data engineering tools, and infrastructure.
- Proficiency in building machine learning models—supervised and unsupervised (e.g., clustering, regression, classification, anomaly detection, time series).
- Expertise in statistical programming languages (e.g., Python), database query languages (e.g., SQL), and ML packages (e.g., scikit-learn, pandas, xgboost).
- Applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, and A/B testing.
- Experience with AWS models (e.g., Lama, Claude, or Amazon Titan).
- Knowledge of NOSQL databases (e.g., Dynamo DB, S3 buckets) and distributed databases (e.g., Snowflake).
- Familiarity with data visualization tools (e.g., Power BI, Tableau).