Senior NLP Engineer

Behavox is hiring a Senior NLP Engineer in Toronto, Canada. It pays CAD 170k-CAD 230k a year and Level rates it ; you can apply on Level.

AI in this role

ragfine-tuningml-opsnlp

About Behavox
Behavox is a global group of companies and a cloud-native AI company providing an integrated controls platform for global banks, asset managers, hedge funds, private equity firms, insurance businesses, and commodity firms. The platform unifies communications and trade surveillance, compliant archiving, policy management, and front-office analytics on a single, AI-native technology stack, delivered as a globally scalable SaaS-based cloud service.
Our engineering culture is built around speed, experimentation, and technical excellence, following agile principles and rapid iteration. We constantly test and adopt the latest cloud technologies and AI tooling, optimizing for fast feedback loops and execution. We look for people who can move fast, challenge conventional wisdom, and want to work at the frontier of modern AI, SaaS platforms, and distributed systems.
Behavox is a high-performance organization with a strong bias toward delivery, ownership, and responsibility. We are building systems that are complex, mission-critical, and global in scale. To do that, we seek highly skilled engineers and technologists who take end-to-end responsibility and want to build what others cannot.
Founded in 2014 and backed by SoftBank Vision Fund, the Behavox group is headquartered in London, with offices worldwide, including New York City, Toronto, Montreal, Seattle, Singapore, and Tokyo.

About the Role
Our Canadian entity, Northern Path Communication & Security Software Inc., is recruiting a Senior NLP Engineer for our Toronto office.
The role sits within the Behavox AI team as an experienced individual contributor. It uses NLP and artificial intelligence (AI) to build features that help our clients understand and act on vast volumes of multilingual communications data (email, chat, and voice transcripts from the world’s largest financial institutions), turning them into accurate, explainable risk and compliance insights.
Alongside model development, the role owns the data foundations behind it. It designs, develops, and maintains the databases and data models that power our NLP pipelines, and sets the standards for how that data is administered across the organization.
Working with a high degree of autonomy, the role takes solutions from prototype to production in close partnership with research scientists, product managers, MLOps, and engineering. It also supports the revenue team with technical expertise when clients ask how our AI works.

What You’ll Do

AI-powered communication features: Use existing NLP technologies and AI solutions, including LLMs, to develop communication assistance features and risk-detection capabilities for clients.
Data science and model development: Design, train, fine-tune, and evaluate NLP models, and take them from prototype to production with MLOps and engineering.
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.
Data administration and testing: Develop and implement data administration policies, standards, and models. Test data models and database management systems before and after release.
Infrastructure improvement: Collect data to identify areas for improvement in the organization’s IT and data infrastructure, and turn findings into practical changes.
Documentation and reporting: Document reporting needs, queries, logic, results, and recommendations for other information systems professionals.
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.

Requirements

Education: Master’s degree in computer engineering or a related field (such as computer science, computational linguistics, or machine learning).
Experience: 5 or more years of experience building NLP, data science, or machine learning systems, including solutions shipped to production.
Language: English.

Skills

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.
Data science: Experience building evaluation datasets and metrics, working with limited and imbalanced data, and explaining model behaviour to non-specialists.
Databases and data modelling: Experience designing, developing, and testing databases, data models, and database management systems.
Programming: Strong Python; working knowledge of C, C++, Go, Java, and JavaScript; daily use of Git.
Production engineering: Experience serving, monitoring, and maintaining models and data systems in production on cloud platforms (AWS or GCP).
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.

Work environment
A 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. What We Offer

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.

About Our Process
We believe hiring is a two-way assessment: you are evaluating us as much as we are evaluating you. Our process looks like this:

Initial conversation: We cover salary, location, core experience and skills, and values alignment.
Technical interview: A deep dive into the technical competencies critical to the role.
Behavioural interview: A deep dive into behavioural competencies.
Practical task: The most aligned candidates complete a work simulation, typically presented and discussed with a group of colleagues and managers.
Leadership meetings: You meet several of our senior leaders so you can make a fully informed decision.

Please note:

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.

How we rate this

Senior NLP Engineer at Behavox rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Senior NLP Engineer is an open job at Behavox.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is CAD 170k-CAD 230k a year.
The role is based in Toronto, Canada.
The job was posted on October 16, 2026.
Seniority: senior.
Applications are handled by Behavox. Apply on Behavox

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGFine-tuningML OpsNLP

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: RAG, Fine-tuning, ML Ops, and NLP. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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