Senior Graph AI Research Engineer
SAP•Montreal, QC
•$108,100 - $222,800•Hybrid
About The Position
We are hiring a Senior Machine Learning Engineer Scientist to lead the development of scalable graph-based and transformer-based modeling systems, along with production-grade ML pipelines. This role sits at the intersection of research and systems engineering and will help shape the next generation of relational foundation model for structured data. You will own key architecture decisions, mentor engineers and researchers, and build high-performance ML systems that operate reliably at scale.
Requirements
- PhD or MS in Computer Science, Machine Learning, Applied Mathematics, Physics, or a related field, with substantial applied experience.
- 5+ years building and delivering ML systems end-to-end.
- Deep expertise in Graph representation learning.
- Deep expertise in Structured / relational modeling.
- Deep expertise in Large-scale training systems.
- Strong hands-on experience with PyTorch.
- Strong hands-on experience with PyTorch Geometric and/or Deep Graph Library (DGL).
- Experience of designing and developing distributed systems and scalable ML infrastructure.
- Advanced Python proficiency and strong software engineering fundamentals.
- Demonstrated ownership of complex ML projects from design through production.
- Experience scaling ML systems in cloud environments (e.g., Azure, AWS).
Nice To Haves
- Experience building foundation models for structured, relational, or graph data.
- Familiarity with transformer architectures tailored to graph and tabular domains.
- Experience with distributed training frameworks (e.g., FSDP, DeepSpeed, Ray).
- Publications in top-tier ML venues (e.g., NeurIPS, ICML, ICLR, KDD).
Responsibilities
- Architect and drive the scalable development of foundation model for relational and graph data.
- Design high-performance data pipelines for large-scale graph, relational, and tabular datasets.
- Establish best practices for experimentation, reproducibility, evaluation, and deployment.
- Define and execute the technical roadmap for ML infrastructure and modeling frameworks.
- Develop and optimize Graph Neural Networks (GNNs), Graph Transformers, and Relational Transformers.
- Develop and optimize Self-supervised, contrastive, and related pretraining strategies for structured data.
- Translate research innovations into robust, production-ready systems.
- Build and operate distributed training and inference pipelines with solid software design & architecture strategy.
- Optimize compute efficiency (GPU/CPU utilization), memory footprint, training throughput, and inference latency.
- Apply or evaluate techniques such as pruning, quantization, architecture search, and model compilation as needed.
- Partner with platform teams to ensure smooth deployment, monitoring, and reliability in production.
- Mentor ML engineers and applied scientists; raise the team’s technical bar through guidance and review.
- Collaborate closely with research, data, and product stakeholders to drive delivery and impact.
Benefits
- Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.
- SAP North America Benefits
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
Upload and Match Resume
What This Job Offers
Job Type
Full-time
Career Level
Senior
