Senior Research Engineer, Machine Learning

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.