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
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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.
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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.
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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.