Senior Research Engineer, Machine Learning

Deep Genomics

Research Engineer developing AI-driven tools for genetic medicine at Deep Genomics. Bridging experimental ML research and robust production systems while optimizing PyTorch code.

Posted 6/18/2026full-timeToronto • 🇨🇦 CanadaSenior💰 CA$175,000 - CA$200,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowCloudDockerGoogle Cloud PlatformKubernetesPyTorch

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PyTorchlinear algebracalculusprobabilitymachine learning architecturesdeep learning architecturesconcurrencythreadingmemory managementdata pipelines

Soft Skills

debuggingtolerance for ambiguitymentoringproblem-solvingarchitectural decision-makingcollaborationindependent hypothesis formulationcommunicationadaptabilitycritical thinking