ML Research Engineer, AI for Life Sciences

This is a fully remote position, open to applicants in Canada.

📋 Description

• Transform innovative concepts and the findings from scientific papers into high-quality, efficient scientific code.

• Spearhead the ideation, benchmarking, and implementation of intricate datasets and machine learning models, ensuring their smooth integration into extensive simulation frameworks.

• Guide the software throughout the entire product lifecycle—from initial research and implementation to launch and ongoing support—maintaining technical excellence at every phase.

⛳️ Requirements

• PhD, or research-oriented MSc, in Computer Science, Physics, Chemistry, or a related quantitative discipline with a focus on advanced computational techniques.

• A minimum of 5 years of industry experience in developing production-level software within professional teams.

• Experience or training in data science tasks related to structural biology.

• Proficiency in converting research papers into tangible machine learning software artifacts.

• Experience supporting models in products that are externally facing, showcasing the ability to connect "research code" with "product code".

• Hands-on experience in biopharma or training in cutting-edge affinity, structure-prediction, or generative chemistry models.

• A proven record of developing and launching successful commercial software products as part of a professional engineering team.

• Knowledge of MLOps practices on major cloud platforms to facilitate automated scaling and model monitoring.

• Experience in interdisciplinary settings where AI intersects with physical or biological sciences.

🏝️ Benefits

• Comprehensive medical, dental, and vision coverage for employees and their dependents, with generous employer premium contributions.

• Retirement savings plan with company matching.

• Paid parental leave.

• Inclusive family-building benefits.

• Flexible paid time off.

• Seasonal breaks for the entire company.

• Support for flexible work arrangements that promote sustainable performance.

• Opportunities for continuous learning and professional growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.