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Natural Language Processing Jobs in Ontario (NOW HIRING)

Td

Full Stack Data Science Engineer

Toronto, ON

CA$120K - CA$154K/yr

Predictive Analytics, Natural Language Processing (NLP), Supervised and Unsupervised Learning, leveraging Generative AI tools and APIs, Model Development and Deployment, Experimentation and ...

THOMSON REUTERS

Senior Research Engineer

Toronto, ON · Hybrid

Exposure to Natural Language Processing (NLP) problems and familiarity with key tasks such as Named Entity Recognition (NER), Information Extraction, Information Retrieval, Text classification ...

Amazon

Applied Scientist, Sales AI

Toronto, ON

You have the scientific and technical skills to build and refine models that can be implemented in production, and you leverage Natural Language Processing and Generative AI models to enhance their ...

Amazon

Applied Scientist, Sales AI

Toronto, ON

... in Natural Language Processing (inc. tokenization, syntactic parsing, named entity recognition (NER), sentiment analysis, text classification), Large Language Models (inc. foundation model ...

Td

Data Scientist - TD Asset Management

Toronto, ON

CA$105K - CA$145K/yr

Theoretical and practical knowledge of machine learning, NLP (natural language processing), deep learning an statistics It would be great if you have: * Strong software engineering skills: version ...

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Natural Language Processing information

See Ontario salary details

$24.5K

$125K

$190K

How much do natural language processing jobs pay per year?

As of Aug 14, 2026, the average yearly pay for natural language processing in Ontario is $125,030.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,000.00 and $166,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in natural language processing, and why are they important?

To thrive in Natural Language Processing, you need strong expertise in linguistics, statistics, and machine learning, typically supported by a degree in computer science, computational linguistics, or a related field. Familiarity with tools and frameworks such as Python, TensorFlow, PyTorch, spaCy, and NLP libraries, as well as certifications in data science or NLP, are valuable assets. Analytical thinking, problem-solving skills, and the ability to collaborate across multidisciplinary teams are highly desirable. These competencies are essential for developing powerful language models, extracting meaningful insights from data, and delivering effective real-world solutions in language technology.

What are some typical challenges faced by professionals in natural language processing?

Professionals in Natural Language Processing (NLP) often encounter challenges such as understanding ambiguities in human language, managing large and unstructured datasets, and keeping up with rapid advances in NLP methodologies. They may also need to fine-tune models for domain-specific contexts and ensure solutions meet ethical and privacy guidelines. Collaboration with data scientists, linguists, engineers, and product teams is common, requiring strong communication skills. Successfully tackling these challenges is a critical part of developing robust NLP applications that add meaningful value to users and businesses.

What is a natural language processing?

A Natural Language Processing (NLP) job involves developing and improving algorithms that enable computers to understand, interpret, and generate human language. Professionals in this field work on tasks like speech recognition, text analysis, machine translation, and chatbot development. They often use machine learning, deep learning, and linguistic principles to build and refine NLP models. NLP experts commonly work in industries such as healthcare, finance, and technology to enhance communication and automate language-related tasks.

Is natural language processing a good career?

Natural Language Processing (NLP) is a growing field within artificial intelligence that involves developing algorithms to understand and generate human language. It offers strong job prospects, competitive salaries, and opportunities to work with machine learning, data analysis, and programming languages like Python. Success in NLP careers often requires a background in computer science, linguistics, or related fields, along with skills in data handling and model development.

What can I do with Natural Language Processing?

A Natural Language Processing (NLP) professional develops systems that enable computers to understand, interpret, and generate human language. This includes tasks like sentiment analysis, language translation, chatbots, and information extraction, often using tools like Python, NLP libraries, and machine learning models. NLP roles require strong programming skills and knowledge of linguistics or data science.

What job categories do people searching Natural Language Processing jobs in Ontario look for?

The top searched job categories for Natural Language Processing jobs in Ontario are:

Natural Language Processing jobs near you
Infographic showing various Natural Language Processing job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 26% Part Time, 1% Temporary, and 4% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $125,030 per year, or $60.1 per hour.

Senior Generative AI Software Engineer

Apertera

Toronto, ON • On-site

Full-time

Re-posted 20 days ago

Job description

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 Senior 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 be working in an R&D Team which reports to the VP of AI Innovation with the objective to develop and implement state-of-the-art algorithms by fast prototyping. We expect our Senior Generative AI Engineer to stay current with the technological cutting edge and drive the application of LLM and GenAI to translation, as well as having solid background and hands-on experience with deep learning, machine learning, natural language processing, and big data. You'll play a pivotal role in pushing the boundaries of applying GenAI to translation scenarios and create innovative solutions.

Responsibilities
  • Research and implement state-of-the-art LLM techniques including continued pre-training, supervised fine-tuning, reinforcement learning from human or AI feedback (PPO, DPO, GRPO, etc.), and LLM deployment.
  • Work closely with our expert advisor to strategize, plan, and design technical roadmaps and features of GenAI products.
  • Develop prototypes of GenAI and LLM application to translation use cases.
  • Drive technological innovations by staying current to the cutting-edge achievements of GenAI and LLM from industry and academia.
  • Stay updated with the latest advancements and research trends in generative AI, attending conferences, workshops, and seminars, and actively contributing to the AI research community through publications and presentations
  • Work closely with DevOps Engineers, software engineers, designers, and product managers to understand project requirements, align on technical solutions, and deliver high-quality generative AI solutions that meet business objectives and user needs.
  • Communicate technical strategies effectively across teams and manage stakeholder expectations. 
Requirements
  • Master in Computer Science, Data Science, Statistics, or Engineering. PhD or equivalent experience is preferred.
  • 3+ years of industry experience developing GenAI and LLM applications.
  • Working knowledge and project-based record of all of the following: context engineering, RAG, SFT. 
  • Working knowledge and project-based record of at least one of the following:  continued pre-training, PPO/DPO/GRPO, Agentic systems (including harness engineering, MCP server, etc.).
  • Proficiency in programming languages such as Python, with experience in software development and version control systems (e.g., Git).
  • Hands-on experience with Huggingface APIs or Amazon Bedrock. Experience with both is preferred. 
  • Expert skills of PyTorch, TensorFlow, Pandas, etc.
  • Experience with cloud platforms like AWS, GCP, or Azure 
  • Excellent problem-solving skills, critical thinking, and the ability to work independently and collaboratively in a fast-paced environment.
  • Strong communication skills, with the ability to articulate complex technical concepts effectively and work cross-functionally with diverse teams.
  • Self-driven, self-motivated with excellent time management skills
  • Excellent organizational, communication, and interpersonal skills
  • Ability to adapt to shifting priorities without compromising deadlines and momentum.

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