Behavox - Senior NLP Engineer
Requirements
• Education: Master’s degree in computer engineering or a related field (such as computer science, computational linguistics, or machine learning). • Education: • Experience: 5 or more years of experience building NLP, data science, or machine learning systems, including solutions shipped to production. • Language: English. • Language: • NLP and AI: Hands-on experience with transformer models and large language models (LLMs), including fine-tuning, retrieval-augmented generation (RAG), and embedding-based search, and with core tasks such as text classification, entity recognition, and relationship extraction. • NLP and AI: • Data science: Experience building evaluation datasets and metrics, working with limited and imbalanced data, and explaining model behaviour to non-specialists. • Data science: • Databases and data modelling: Experience designing, developing, and testing databases, data models, and database management systems. • Databases and data modelling: • Programming: Strong Python; working knowledge of C, C++, Go, Java, and JavaScript; daily use of Git. • Programming: • Production engineering: Experience serving, monitoring, and maintaining models and data systems in production on cloud platforms (AWS or GCP). • Production engineering: • Personal suitability: Accurate, organized, and detail-oriented, with sound judgment, initiative, and accountability. Adaptable, a quick learner, and a team player with a strong client focus and excellent written and oral communication. • Personal suitability: • Work environmentA fast-paced environment with tight deadlines, where you will sometimes work under pressure. Attention to detail matters because our clients operate in highly regulated industries. • Work environment
Responsibilities
• AI-powered communication features: Use existing NLP technologies and AI solutions, including LLMs, to develop communication assistance features and risk-detection capabilities for clients. • AI-powered communication features: • Data science and model development: Design, train, fine-tune, and evaluate NLP models, and take them from prototype to production with MLOps and engineering. • Data science and model development: • Database design and development: Design, develop, and maintain the databases behind our NLP pipelines, and develop and implement a strategic blueprint for deploying databases across the organization. • Database design and development: • Data administration and testing: Develop and implement data administration policies, standards, and models. Test data models and database management systems before and after release. • Data administration and testing: • Infrastructure improvement: Collect data to identify areas for improvement in the organization’s IT and data infrastructure, and turn findings into practical changes. • Infrastructure improvement: • Documentation and reporting: Document reporting needs, queries, logic, results, and recommendations for other information systems professionals. • Documentation and reporting: • Collaboration and mentoring: Advise product and engineering teams, explain model behaviour to clients alongside the revenue team, and mentor other engineers through Git-based workflows and code review. • Collaboration and mentoring:
Benefits
• Salary: $170,000 to $230,000 CAD per year, depending on experience. • A benefits package that includes an equity award aligned with long-term value creation, in addition to cash compensation. • 30 days of paid time off annually, plus public holidays and sick leave. • A hybrid role based in our modern Toronto office (1 day per week in office). • The opportunity to work on a global, mission-critical AI platform alongside highly skilled engineers and technologists across multiple geographies. • Real ownership and impact, building complex systems at scale in an environment that values speed, experimentation, and technical excellence. • Initial conversation: We cover salary, location, core experience and skills, and values alignment. • Initial conversation: • Technical interview: A deep dive into the technical competencies critical to the role. • Technical interview: • Behavioural interview: A deep dive into behavioural competencies. • Behavioural interview: • Practical task: The most aligned candidates complete a work simulation, typically presented and discussed with a group of colleagues and managers. • Practical task: • Leadership meetings: You meet several of our senior leaders so you can make a fully informed decision. • Leadership meetings: • We want a genuine conversation, so the use of AI tools or assistance during live interviews is not permitted and will result in disqualification from the process. • Interviews may be recorded for internal review, to support fairness and collaborative hiring discussions within the team.
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