Machine Learning Research Engineer developing AI solutions at RBC Borealis, focusing on innovative machine learning projects. Join a team of experts in applying machine learning to real - world problems.
LP
Sr. Applied ML Specialist, Research Eng
Posted via LinkedIn Recruiter (not a company profile)
Posted 4 months ago
About the role
- Senior Applied ML Specialist building scalable ML research infrastructure to accelerate applied research. Implement research prototypes into robust systems and develop tooling for the full ML lifecycle.
Responsibilities
- Design, build, and maintain scalable ML research infrastructure including training pipelines, experiment orchestration, evaluation harnesses, and data processing systems. Implement and extend ML research prototypes from papers and internal work, taking them from proof-of-concept to robust, reproducible systems. Develop internal tooling and libraries for data ingestion, preprocessing, training, fine-tuning, benchmarking, and results tracking. Scale applied research efforts with efficient pipelines for multi-dataset, multi-model, and distributed compute workloads. Build and ship open-source research software, reference implementations, and model toolkits. Collaborate with Applied ML Scientists to translate research requirements into engineering specifications. Own complex research engineering initiatives from architecture to delivery. Communicate progress through documentation, demos, and presentations.
Requirements
- Bachelor’s degree in computer science, mathematics, electrical engineering, or related discipline; MSc/MEng preferred. Minimum 4 years experience in research engineering, ML infrastructure, or applied ML with track record of building systems that accelerate research. Demonstrated experience as technical lead on research engineering or applied ML projects. Strong proficiency in Python with emphasis on clean, well-tested, reusable research code. Hands-on experience building ML training and evaluation pipelines with large-scale datasets. Deep familiarity with ML frameworks (PyTorch, HuggingFace Transformers, JAX). Experience with CUDA or GPU optimization. Strong command of ML tooling ecosystem (MLflow, W&B, model evaluation, dataset versioning, model registries). Experience with distributed training, multi-GPU/multi-node compute orchestration, cloud-native infrastructure (Kubernetes, Docker, GCP/AWS/Azure). Familiar with full ML research lifecycle. Experience contributing to open-source ML libraries or shared internal tooling preferred. Strong written and verbal communication skills.
Benefits
- Vacation time, floater days, GRRSP, Health Spending Account, Summer Hours program, flexible work arrangements.
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