Senior Graph AI Research Engineer

SAP; Montreal (administrative region); On-site; CAD 150,000 - 200,000; Full time

Benefits offered by this job

Constant learning opportunities

Skill growth programs

Great benefits

Inclusive work culture

Job summary

A leading software company is looking for a Senior Machine Learning Engineer in Montreal to lead the development of scalable ML systems. The role involves mentoring engineers and optimizing data pipelines, requiring a PhD or MS in a relevant field and 5+ years of experience in ML. You will define technical roadmaps and collaborate with stakeholders. Competitive compensation of CAD 150,000 - 200,000 is offered. This is a great opportunity to impact AI advancement in a dynamic team environment.

Qualifications

• PhD or MS degree with substantial applied experience in ML systems.
• 5+ years of experience building ML systems end-to-end.
• Experience designing distributed systems and scalable ML infrastructure.

Responsibilities

• Lead development of scalable graph-based and transformer-based modeling systems.
• Architect high-performance data pipelines for large-scale datasets.
• Mentor ML engineers and applied scientists.

Skills

Graph representation learning

Structured / relational modeling

Large-scale training systems

PyTorch

Advanced Python proficiency

Education

PhD or MS in Computer Science, Machine Learning, Applied Mathematics, Physics, or related field

Tools

PyTorch Geometric

Deep Graph Library (DGL)

Job description

We help the world run better

At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

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 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

Technical Leadership

• 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.

Model Development

• 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.

Scalable Systems & Infrastructure

• 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.

Collaboration & Mentorship

• 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.

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.
• Deep expertise in:
  • Graph representation learning
  • Structured / relational modeling
  • Large-scale training systems
• Strong hands‑on experience with:
  • PyTorch
  • 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.

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).
• Publications in top‑tier ML venues (e.g., NeurIPS, ICML, ICLR, KDD).
• Experience scaling ML systems in cloud environments (e.g., Azure, AWS).

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.

Bring out your best

SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end‑to‑end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose‑driven and future‑focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.

We win with inclusion

SAP’s culture of inclusion, focus on health and well‑being, and flexible working models help ensure that everyone – regardless of background – feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world.

SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e‑mail with your request to Recruiting Operations Team: [e-mail].

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

Compensation Range Transparency

SAP believes the value of pay transparency contributes towards an honest and supportive culture and is a significant step towards demonstrating SAP’s commitment to pay equity. SAP provides the annualized compensation range inclusive of base salary and variable incentive target for the career level applicable to the posted role. The targeted combined range for this position is 150000 - 200000(CAD) USD. The actual amount to be offered to the successful candidate will be within that range, dependent upon the key aspects of each case which may include education, skills, experience, scope of the role, location, etc. as determined through the selection process. Any SAP variable incentive includes a targeted dollar amount and any actual payout amount is dependent on company and personal performance. Please reference this link for a summary of SAP benefits and eligibility requirements: SAP North America Benefits.

AI Usage in the Recruitment Process

For information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process. Please note that any violation of these guidelines may result in disqualification from the hiring process.