Sr. Machine Learning Engineer
| Company | Workday |
|---|---|
| Location | Toronto, ON, Canada, Pleasanton, CA, USA, Beaverton, OR, USA, Boulder, CO, USA, Atlanta, GA, USA, Vancouver, BC, Canada |
| Salary | $167200 – $297600 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior |
Requirements
- 5+ yrs experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
- 3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
- 3+ years of professional experience in building services to host machine learning models in production at scale
- 2+ years of professional experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
- 2+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)
- Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
- Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
- Professional experience in independently solving ambiguous, open-ended problems and technically leading teams
- Strong communication skills, with experience working across functions and teams. Ability to teach, mentor and lead through influence
Responsibilities
- Own exploration, design and execution of advanced ML models, algorithms and frameworks that deliver value to our users.
- Apply machine learning techniques including LLMs, knowledge graphs, deep learning including generative models, natural language understanding, and named entity recognition to analyze large sets of HR and Finance-related text data, and design and launch pioneering cloud based machine learning architectures.
- Own the performance, scalability, metric based deployed evaluation, and ongoing data driven enhancements of your products.
- Keep abreast of the latest advancements in NLP research, techniques, and tools and apply this knowledge onto ML Features.
Preferred Qualifications
- Exposure to advanced techniques such as reinforcement learning and graph neural networks
- Relevant PhD and/or machine learning related research publications will be a plus