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
- Architecting and optimizing leading-edge ML and physics-based models at SandboxAQ. Driving research to production for drug discovery and materials science.
Responsibilities
- Bring content of scientific papers into promising, scalable ML algorithms; and translate these into high-performing and robust scientific code
- Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into our large-scale simulation frameworks
- Implement advanced software and hardware optimizations to maximize the efficiency of ML pipelines across distributed cloud GPU environments
- Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage
Requirements
- MSc (PhD preferred) in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods
- Senior (5+ years) industry experience developing productionized software in professional teams
- Proven experience training and optimizing large-scale ML pipelines on distributed cloud GPUs (e.g. PyTorch, TensorFlow)
- Deep familiarity with agentic coding tools (e.g. Claude code, Codex)
- Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between "research code" and "product code".
- Direct experience in biopharma or training leading-edge affinity, structure-prediction, or generative chemistry models (highly desired)
- A history of developing and launching successful commercial software products within a professional engineering team (highly desired)
- Familiarity with MLOps practices on major cloud platforms to support automated scaling and model monitoring (highly desired)
- Experience working in interdisciplinary environments where AI intersects with physical or biological sciences (highly desired)
Benefits
- Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
- Retirement savings with company matching
- Paid parental leave and inclusive family-building benefits
- Flexible paid time off
- Company-wide seasonal breaks
- Support for flexible work arrangements that enable sustainable performance
- Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs
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