Senior AI Machine Learning Engineer

SAP SE; Montreal (administrative region); On-site; CAD 108,100 - 222,800; Full time

Job summary

SAP SE is seeking a Senior Machine Learning Engineer Scientist to develop scalable modeling systems focused on structured data. This role includes technical leadership to drive the architecture of ML systems and mentor a team of engineers while optimizing performance in production environments.

You will have significant experience with Python and machine learning frameworks such as PyTorch. The position offers a competitive salary range of 108,100 - 222,800 CAD USD based on your skills and experience.

Qualifications

• 5+ years of experience building and delivering ML systems end-to-end.
• Experience scaling ML systems in cloud environments (e.g., Azure, AWS).
• Demonstrated ownership of complex ML projects from design through production.

Responsibilities

• Lead the development of scalable graph-based modeling systems.
• Design high-performance data pipelines for large-scale datasets.
• Optimize compute efficiency and memory footprint of ML systems.

Skills

Machine Learning

PyTorch

Graph Neural Networks

Distributed Systems

Python

Education

PhD or MS in Computer Science or related field

Tools

PyTorch Geometric

Deep Graph Library (DGL)

Job description

About the Role

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.

What You’ll Do

• Technical Leadership
  • Architect and drive the scalable development of foundation models 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.
• Geometric ML Knowledge
  • Develop and optimize Graph Neural Networks (GNNs), Graph Transformers, and Relational Transformers.
  • Apply self‑supervised, contrastive, and related pre‑training strategies for structured data.
  • Translate research innovations into robust, production‑ready systems.
• Scalable Systems & Infrastructure
  • Build and operate distributed training and inference pipelines with solid software design and 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.

Required Qualifications

• 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.
• Strong hands‑on experience with PyTorch, PyTorch Geometric and/or Deep Graph Library (DGL).
• Experience 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).

Preferred Qualifications

• 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).
• Deep expertise in graph representation learning and structured / relational modeling.
• Publications in top‑tier ML venues (e.g., NeurIPS, ICML, ICLR, KDD).

What We’re Looking For

• A systems‑minded scientist who bridges research depth with engineering rigor.
• A strong architectural thinker who can design scalable solutions with long‑term maintainability.
• A leader who mentors others and elevates engineering and research standards.
• Passion for advancing structured‑data AI and building platforms that deliver real‑world impact.

Compensation

The targeted combined range for this position is 108,100 - 222,800 CAD USD. The actual amount offered will be within that range, depending on education, skills, experience, scope of the role, location, and other factors. Variable incentive is a target amount; actual payout depends on company and personal performance.

Equal Employment Opportunity Statement

Qualified applicants will receive consideration for employment without regard to age, race, religion, national origin, ethnicity, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability.