Senior Research Engineer, Machine Learning

Deep Genomics•Toronto, ON

37d•CA$175,000 - CA$200,000•Hybrid

About The Position

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team in Toronto and Cambridge, MA is revolutionizing how new medicines are created. You are a research engineer who bridges the gap between fast-paced, experimental academic ML research and robust production systems. You possess a solid mathematical foundation and a deep understanding of modern ML architectures, combined with the technical skills to write clean and highly optimized PyTorch code. You thrive in ambiguity and are perfectly comfortable diving into messy research scripts to refactor, optimize, and build out the necessary scaffolding. You understand the delicate balance between rapid iteration and technical debt, and you take pride in creating core tooling that empowers research teams to move fast without breaking everything. Above all, you are a critical thinker and collaborative problem solver eager to push the boundaries of research.

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.

Nice To Haves

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.

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.

Benefits

Your engineering work will directly impact the creation of new genetic medicines for patients with unmet needs. Models you optimize and deploy will be central in collaborations with drug developers and our established pharmaceutical partners.
The opportunity to build Biological Foundation Models that map genetic inputs to downstream molecular mechanisms, and ultimately patient outcomes.
Immersion in a new scientific domain. No prior biology expertise is required. You will partner with both computational biologists and wet-lab scientists, gaining the scientific context needed to maximize your engineering impact.
Opportunities to publish and present work focusing on AI for genome biology and medicine.
A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery.
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.