Generative AI Software Engineer
Company: Apertera
Location: Toronto, ON
Type: Full-time
Posted: 2026-06-05
About this role
About Apertera
Apertera is leading the evolution of language solutions for high-stakes content. We partner with enterprises as an extension of their teams, combining professional expertise with Adaptive AI technology that is continuously refined by client context.
For more than twenty years, Apertera has set the bar for legal, financial, and regulatory translation, serving the most rigorous buyers, including over 75% of major national Canadian law firms, all major banks, and leading securities regulators.
Apertera is Canadian-owned, ISO 17100 and SOC 2 certified.
Our core values:
- Innovation
- Dedication
- Fanatical commitment to quality and service
- Resourcefulness
- Collaboration
About the Role
We are looking for a Generative AI Engineer to develop our next-generation intelligent translation and translation-related service engine, using Generative AI (GenAI) and Large Language Model (LLM) technologies. You will report to the team lead on GenAI, develop and implement state-of-the-art algorithms by fast prototyping, and collaborate with the software team to deploy models. We expect our Generative AI Engineer to stay current with the technological cutting edge and build applications of LLM and GenAI to machine translation with best industry practices, as well as having solid background and hands-on experience with deep learning, machine learning, natural language processing, and big data.
Responsibilities
- Research and implement state-of-the-art LLM techniques including continued pre-training, instruction fine-tuning, preference alignment, and LLM deployment while also focusing on prompt engineering and GenAI more broadly.
- Work closely with machine learning engineers and data scientists to design, build, and test models.
- Contribute to technological innovations by staying current to the cutting-edge achievements of GenAI and LLM from industry and academia.
- Develop efficient and scalable algorithms for training and inference of gener...