SAP Software Solutions | Business Applications and Technology seeks a Senior Machine Learning Engineer Scientist to lead the development of scalable graph-based and transformer-based modeling systems and production-grade ML pipelines. This role sits at the intersection of research and systems engineering and will help shape the next generation of structured-data intelligence platforms. You will own key architecture decisions, mentor engineers and researchers, and build high-performance ML systems that operate reliably at scale.
What You'll Do
- Architect and drive the development of foundation-style models for structured, 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.
- Lead benchmarking and performance optimization across datasets and real-world use cases.
- Build and operate distributed training and inference pipelines.
- 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.
- Contribute to hiring, interview loops, and technical standards across the organization.
What We're Looking For
- 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 designing distributed systems and scalable ML infrastructure.
- Advanced Python proficiency and strong software engineering fundamentals.
- Demonstrated ownership of complex ML projects from design through production.
Nice to Have
- 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).
- Experience scaling ML systems in cloud environments (e.g., Azure, AWS).
Technical Stack
- PyTorch
- PyTorch Geometric
- Deep Graph Library (DGL)
- Python
- Azure, AWS
- FSDP, DeepSpeed, Ray
Benefits & Compensation
- Compensation: $150,000 - $200,000 (CAD) USD
Work Mode
This is a hybrid position.
Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, age, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability.
