Machine Learning Research Engineer at RBC Borealis applying ML technology to complex problems with a focus on generative AI, NLP, and time series analysis.
About the role
- Research Engineer developing AI-driven tools for genetic medicine at Deep Genomics. Bridging experimental ML research and robust production systems while optimizing PyTorch code.
Responsibilities
- Develop and Optimize Core Tooling: Build and maintain the engineering infrastructure that allows the research team to iterate rapidly and safely.
- Bridge Research and Engineering: Refactor and optimize experimental, script-like research code, adding necessary scaffolding and engineering rigor without stifling discovery.
- Model Implementation: Implement, train, and evaluate modern deep learning architectures using PyTorch.
- Testing and Debugging: Rigorously test and troubleshoot complex ML systems to ensure both software correctness and optimal computational efficiency.
- Navigate Trade-offs: Continuously balance the need for research speed with the realities of technical debt, making pragmatic architectural decisions.
Requirements
- Solid foundational grasp of linear algebra, calculus, and probability.
- Strong understanding of modern machine learning/deep learning architectures and training dynamics.
- High proficiency in PyTorch, including model building and basic optimization.
- Strong general programming skills, with practical experience handling concurrency, threading, and memory management.
- Demonstrated ability to debug software correctness and computational performance.
- High tolerance for ambiguity and a willingness to work hands-on with unstructured research code.
- Domain knowledge or a strong interest in computational biology.
- Familiarity with ML experiment tracking tools (e.g., Weights & Biases) and workflow orchestration concepts (e.g., Airflow).
- Knowledge of Kubernetes, containerization (Docker), and deploying workloads on cloud platforms (e.g., GCP).
- Experience handling, processing, and optimizing large-scale data pipelines.
- Ability to read dense, math-heavy research papers, spot theoretical flaws or computational bottlenecks, and implement them independently from scratch.
- Extensive knowledge of PyTorch internals, distributed training paradigms, custom operators (e.g., CUDA/Triton kernels), and advanced performance profiling.
- Deep intuition for ML failure modes. Can independently formulate hypotheses to diagnose convergence issues, data bottlenecks, or complex edge-case model behaviours.
- Mentors researchers on engineering best practices, establishing team-wide guardrails and templates without slowing down their iteration cycles.
- Owns "Build vs. Buy" and open-source adaptation strategies, making high-stakes architectural decisions that shape the 1-2 year technical roadmap.
- Proven experience partnering closely with dedicated MLOps and Data Engineering teams to seamlessly transition research models into existing production pipelines.
Benefits
- Highly competitive compensation, including meaningful stock ownership.
- Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program.
- Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
- Maternity and parental leave top-up coverage, as well as new parent paid time off.
- Focus on learning and growth for all employees - learning and development budget & lunch and learns.
- Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.
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