Principal Machine Learning Developer
| Company | Autodesk |
|---|---|
| Location | Montreal, QC, Canada, Toronto, ON, Canada, Vancouver, BC, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior, Expert or higher |
Requirements
- BS or MS in Computer Science, or equivalent practical experience
- 8+ years of experience in software development and engineering, with a solid record of delivering production systems and services
- Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks
- Hands-on experience with AI/ML frameworks (such as TensorFlow, PyTorch) and familiarity with the lifecycle of AI/ML model development, from training to deployment
- Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go
- Ability to tackle complex technical challenges, analyze potential solutions, and implement the most effective ones
- Strong communication skills to effectively collaborate with cross-functional teams, along with the ability to work independently
- Deep understanding of performance metrics and latency optimization techniques, with the ability to diagnose, tune, and enhance the efficiency of serving systems
- A continuous learning mindset to stay updated with the latest trends and technologies in AI/ML, cloud computing, and software engineering
Responsibilities
- Dig deep into the data processing pipelines and model training architectures of our customer teams and help inform platform design decisions based on empathy
- Implement monitoring tools and practices to track the performance of AI/ML models during training and in production, identifying waste, bottlenecks, and optimizing system and model performance for better efficiency and reduced costs
- Oversee the deployment of AI/ML models into production, including the setup of CI/CD pipelines for model deployment and versioning, ensuring smooth and reliable model updates and rollbacks
- Stay abreast of the latest developments in AI/ML technologies, cloud computing, and MLOps practices, exploring and integrating innovative solutions that can enhance the capabilities and efficiency of the AI/ML serving platform
Preferred Qualifications
- Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance
- Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment
- Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)
- Experience with HPC techniques and technologies for optimizing computational workloads, particularly in the context of AI/ML model training and inference