Lead Applied Scientist – Machine Learning, NLP & Generative AI - 220k-240k CAD, 25% AIP, 30% LTI
A global technology-led organisation is seeking a Lead Applied Scientist to join its applied research function. This role sits at the intersection of machine learning research and real-world product delivery , with a strong focus on NLP, Information Retrieval, and Generative AI .
This is a senior, hands-on position suited to candidates who enjoy owning AI solutions end to end , from problem definition and experimentation through to production deployment and business impact.
The Role:
- Leading the design and delivery of ML, NLP, IR, and Generative AI systems
- Applying information retrieval techniques, context engineering, model development, and evaluation design
- Driving applied research into production-ready AI solutions
- Partnering closely with Product, Engineering, UX, and business stakeholders
- Influencing long-term AI and applied research strategy
- Developing deep understanding of customer problems and data
- Mentoring and coaching Applied Scientists and ML Engineers
- Contributing to publications, intellectual property, and technical thought leadership
- Participating in and attending leading AI conferences (e.g. NeurIPS, ICLR, ACL, EMNLP, NAACL, SIGIR, KDD)
Essential criteria:
- PhD in Computer Science, AI, Machine Learning, NLP, or a related discipline
- or a Master’s degree with equivalent industry experience
- 7+ years of hands-on experience building ML / NLP / IR systems in production
- Strong expertise in Natural Language Processing, Information Retrieval, and/or Generative AI
- Proven ability to architect and deliver end-to-end AI solutions
- Experience writing and maintaining production-quality code
- Demonstrated success translating complex research problems into business-ready AI applications
- Experience leading through others in an applied research or industry R&D environment
- Excellent communication and stakeholder management skills
- Comfortable working in agile, fast-paced environments with a focus on impact and iteration
