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Neural Engineering Jobs in Ontario (NOW HIRING)

ABR Overview Applied Brain Research has been a prominent developer of advanced AI solutions for almost a decade and is now developing commercial products based upon its patented state space neural ...

Summer Intern 2027 - AI

Toronto, ON · Hybrid

CA$54K - CA$72K/yr

Exposure to machine learning techniques such as regression, tree-based models, neural networks, or time series methods * Familiarity with generative AI concepts (e.g., prompt engineering, retrieval ...

New

Partner with engineering, operations, and cross-functional teams to ensure seamless model ... neural networks (CNN, RNN, LSTM, Transformers), k-means clustering, DBSCAN, decision trees (CART ...

Distillation Lead

Toronto, ON

CA$195K - CA$286K/yr

... neural networks, with demonstrated impact in production settings. - Strong research and engineering foundation: A Bachelor's or Master's degree in Machine Learning, Computer Vision, Robotics, or a ...

Partner with engineering, operations, and cross-functional teams to ensure seamless model ... neural networks (CNN, RNN, LSTM, Transformers), k-means clustering, DBSCAN, decision trees (CART ...

Showing results 41-60

Neural Engineering information

See Ontario salary details

$30K

$119.9K

$200.5K

How much do neural engineering jobs pay per year?

As of Sep 3, 2026, the average yearly pay for neural engineering in Ontario is $119,871.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,000.00 and $163,000.00 per year, depending on experience, location, and employer.

What is neural engineering?

Neural engineering is a multidisciplinary field that combines engineering, neuroscience, and computational approaches to understand, repair, enhance, or interface with the nervous system. Neural engineers develop devices such as brain-computer interfaces, neural prosthetics, and neurostimulation systems to restore or improve neural function. This field plays an important role in advancing treatments for neurological disorders and in creating technologies that bridge the gap between machines and the human brain.

What are jobs in neural engineering?

Jobs in neural engineering focus on helping research and design biomedical devices like prosthetic limbs and artificial organs. In these roles, you may determine the best way to implement designs for each situation, figure out the best way to link mechanical systems to the human brain, and find the most cost-effective ways to build devices. Neural engineering differs from engineering regular prosthetic limbs in that they receive instructions directly from the brain and often send information back, rather than simply being attached to the body. This often involves programming specialized software and figuring out how to make devices that can teach the brain how to use them. In recent years, neural engineering has started to move out of the medical realm, and there may be more jobs of that nature in the future. Neural engineering is a specific type of biomedical engineering, but should not be confused with jobs in the broader category.

What are the key skills and qualifications needed to thrive as a neural engineer, and why are they important?

To thrive as a Neural Engineer, you need a strong background in neuroscience, biomedical engineering, and signal processing, typically supported by an advanced degree in a related field. Familiarity with programming languages (such as MATLAB or Python), neuroimaging tools, and hardware platforms used for neural interfacing is essential. Excellent problem-solving skills, collaboration, and clear communication set standout professionals apart in this multidisciplinary environment. These skills are crucial for developing innovative neural technologies and translating research into effective clinical or commercial solutions.

What are some common interdisciplinary challenges faced by neural engineers when collaborating with clinicians and data scientists?

Neural engineers frequently work on teams that include clinicians, data scientists, and hardware specialists, which can present unique interdisciplinary challenges. Effective communication is essential, as team members often have different technical backgrounds and priorities—clinicians focus on patient outcomes, while data scientists emphasize analytical accuracy. Bridging the gap between clinical needs and technical feasibility requires adaptability, openness to feedback, and a willingness to learn new concepts. Building strong collaborative relationships and participating in regular cross-functional meetings can help ensure that project goals are clearly understood and met by all stakeholders.

How much do neural engineers make?

Neural engineers typically earn a median annual salary of around $80,000 to $120,000, depending on experience, education, and location. Advanced skills in neurotechnology, programming, and biomedical engineering can lead to higher compensation, especially in research or industry roles.

Is neural engineering a good career?

Neural engineering is a growing field that combines neuroscience, engineering, and technology to develop medical devices and brain-computer interfaces. It offers opportunities in research, healthcare, and industry, often requiring advanced degrees and technical skills. The career can be rewarding for those interested in innovation and interdisciplinary work.

What can you do with a neural engineering degree?

A neural engineering degree prepares individuals for careers in developing brain-computer interfaces, neuroprosthetics, and neural signal processing. Graduates often work in research, healthcare, or industry settings, utilizing skills in neuroscience, engineering, and programming tools like MATLAB or Python. Opportunities include roles in biomedical device development, neural data analysis, and clinical applications.

What are popular job titles related to Neural Engineering jobs in Ontario?

For Neural Engineering jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Neural Engineering jobs in Ontario look for?

The top searched job categories for Neural Engineering jobs in Ontario are:

Infographic showing various Neural Engineering job openings in Ontario as of August 2026, with employment types broken down into 84% Full Time, 12% Part Time, 3% Contract, and 1% Nights. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $119,871 per year, or $57.6 per hour.

Senior Machine Learning Engineer

Fitch Solutions

Toronto, ON • On-site

Full-time

Re-posted 20 days ago


Job description

Senior Machine Learning Engineer - AI Innovation Teams

As one of the world's top three credit ratings agencies, Fitch Ratings plays a critical role in global capital markets by providing credit analysis, ratings, research, and commentary to financial market participants. For over 100 years, Fitch Ratings has been creating value for global markets through its rigorous analysis and deep expertise, which have resulted in a variety of market leading tools, methodologies, indices, research, and analytical products. Fitch Ratings is part of Fitch Group, a global leader in financial information services with operations in more than 30 countries, which also includes Fitch Solutions. With dual headquarters in London and New York, Fitch Group is owned by Hearst.

Join our fastgrowing Toronto innovation hub, where we're building productiongrade AI to reshape global credit decisions. Here, you won't just ship features-you'll help reinvent an industry using decadesdeep proprietary data and a modern tech stack, free from heavy legacy. Backed by full enterprise support, you'll build reliable, explainable intelligence at real scale within a collaborative, outcomedriven community. If you're looking for purposeful work, constant learning, and a place where your impact and growth accelerate-this is where you'll thrive.

Want to learn more about our Toronto Innovation Hub? Visit: https://careers.fitch.group/go/Toronto-Innovation-Hub/9713801/

Fitch Ratings is seeking a Senior Machine Learning Engineer to join our new AI Innovation teams in Toronto-where we're building the AI-powered future of financial analysis from the ground up. This isn't about tweaking hyperparameters or maintaining legacy models. This is about designing and shipping breakthrough generative AI systems, agentic workflows, and intelligent platforms that will transform how credit analysis happens and how global financial markets operate.

We're at a defining moment. Fitch is making a major strategic bet on AI, investing heavily in Toronto as our innovation center, and we're building teams of exceptional ML engineers to turn ambitious vision into production reality. As a Senior ML Engineer, you'll be a technical force on the team-building sophisticated ML systems, driving innovation through hands-on work, mentoring engineers, and helping establish the technical standards that will scale across the organization. You're joining early enough to shape how we build, with the resources and runway to do it right.

We need ML engineers who thrive on hard problems and greenfield opportunities-whether you're passionate about pushing the boundaries of what LLMs can do, excited to architect intelligent systems that reason and act, or energized by turning cutting-edge research into production capabilities. If you're motivated by "let's build this and see what's possible" rather than "let's wait and see what others do," this is a high-impact role where you'll spend your time shipping transformative ML systems-working alongside talented engineers who are equally committed to building something significant.

What We Offer:

  • Build breakthrough ML systems with real impact - Design and ship production generative AI platforms, multi-agent systems, and intelligent automation that will process billions in credit decisions; work on problems at the frontier of applied AI while seeing your work directly change how analysts and financial markets operate
  • Work at the cutting edge of ML technology - Experiment with the latest LLMs and foundation models, implement novel RAG architectures, build agentic systems, fine-tune neural networks, and leverage enterprise-scale GPU clusters and cloud infrastructure; substantial conference and training budgets to stay at the forefront
  • Technical leadership without the bureaucracy - Lead projects, mentor junior engineers, shape technical direction, and influence architectural decisions through your expertise and results-not through management hierarchy; your ideas and code will define how we build AI systems
  • Toronto's world-class AI ecosystem - Work in one of the world's premier AI research hubs alongside Vector Institute researchers, attend cutting-edge ML meetups and conferences, and be part of the community defining the future of applied AI and machine learning
  • Greenfield innovation with enterprise backing - Build net-new ML systems from scratch with the freedom to experiment boldly, fail fast, and push boundaries-backed by the compute resources, research budgets, and organizational support that most startups can only dream of
  • Solve sophisticated ML challenges - Tackle hard problems at the intersection of NLP, document intelligence, reasoning systems, agentic workflows, and production-scale deployment; work on challenges that will expand your expertise and push your technical boundaries
  • Clear growth trajectory - High visibility to senior leadership, mentorship from experienced ML architects, and clear paths to Lead ML Engineer or Principal ML Engineer roles; build a reputation as a go-to expert in generative AI and financial technology
 We'll Count on You To:
  • Design and build transformative ML systems - Lead the development of advanced generative AI solutions, agentic workflows, RAG architectures, and intelligent platforms using PyTorch, modern ML frameworks, and large language models; write production-quality code that scales and performs
  • Drive AI innovation through hands-on technical work - Experiment with frontier models, implement novel ML architectures, evaluate emerging AI technologies, build proofs-of-concept, and translate cutting-edge research into production capabilities that deliver real business value
  • Lead projects and drive technical excellence - Take ownership of significant ML initiatives from design through deployment; make architectural decisions, establish coding standards, implement robust CI/CD for ML systems, and ensure solutions are both innovative and reliable
  • Mentor and elevate junior engineers - Provide technical guidance, conduct code reviews, share ML best practices, and help junior team members grow their skills; foster a culture of learning, experimentation, and technical excellence
  • Build production ML infrastructure - Develop scalable APIs (FastAPI, etc.) for model deployment, implement MLOps pipelines, leverage cloud platforms (AWS/Azure) to optimize AI infrastructure, and use orchestration tools (Airflow) for complex ML workflows
  • Champion ML governance and operational excellence - Ensure adherence to AI/ML governance guidelines, monitor model performance and SLAs, optimize systems for reliability and cost-effectiveness, and implement best practices for production ML systems
  • Collaborate across teams effectively - Partner with product squads, business stakeholders, and cross-functional teams to integrate ML solutions into flagship products and workflows; translate complex ML concepts for diverse audiences; and ensure seamless transitions from prototype to production
  • Stay at the bleeding edge - Continuously explore emerging ML technologies, attend conferences, contribute insights from research, and bring innovative approaches back to the team; help shape our technical strategy through your expertise and experimentation
 What You Need to Have:
  • Strong ML engineering expertise - 6+ years of professional experience building production AI/ML systems, with demonstrated ability to deliver advanced generative AI and ML solutions from concept through deployment
  • Advanced generative AI and LLM experience - Extensive hands-on experience developing and integrating generative AI solutions, working with large language models, building agentic workflows, implementing RAG architectures, and integrating AI capabilities into existing products and systems
  • Deep ML technical skills - Strong proficiency in Python and ML algorithms ranging from classical techniques to deep learning; proven experience training, fine-tuning, and deploying neural network models using PyTorch with focus on performance optimization and scalability
  • Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics, or related field (Master's or PhD is strongly preferred)
  • Production ML engineering excellence - Strong understanding of MLOps, containerization (Docker, Kubernetes/AWS EKS), cloud platforms (AWS/Azure including Bedrock, SageMaker, Azure AI Search), workflow orchestration (Airflow), and API development for ML systems
  • Software development fundamentals - Deep understanding of automated testing, source version control, code optimization, software architecture, and building scalable, maintainable systems
  • Technical leadership through influence - Track record of leading project initiatives, mentoring team members, shaping technical strategy without direct management, and driving innovation in fast-paced environments
  • Outstanding collaboration and communication - Ability to work effectively with technical and non-technical stakeholders, translate complex ML concepts for diverse audiences, and foster alignment across distributed cross-functional teams
 What Would Make You Stand Out:
  • Cutting-edge AI implementation experience - Track record of taking breakthrough AI capabilities from research/prototype to production-scale deployment; experience supporting seamless transitions from experimentation to enterprise-grade ML systems
  • Multi-agent and agentic systems expertise - Hands-on experience building sophisticated multi-agent systems, agentic workflows, tool-using AI, or complex AI orchestration platforms that demonstrate advanced reasoning and autonomy
  • MLOps and cloud-native infrastructure - Advanced expertise building ML infrastructure, sophisticated MLOps pipelines, model serving platforms, and optimizing cost/performance of production LLM deployments at scale
  • Document and content management systems experience - Experience developing or integrating ML functionality for document management systems, content platforms, or document intelligence solutions
  • Technical thought leadership - Contributions to open source ML projects, conference presentations, published research, blog posts about practical ML applications, or active participation in the ML engineering community
  • Financial services or analytical domain knowledge - Understanding of credit analysis workflows, regulatory requirements, financial data products, or how ML enables better financial decision-making; familiarity with ratings agencies is valuable
  • Startup or innovation environment experience - History of building greenfield ML products, working in fast-paced AI innovation teams, or being part of early-stage initiatives where you helped shape technical direction and culture
  • Toronto AI/ML community involvement - Active participation in Toronto's AI/ML engineering or research communities, connections to academic groups, or strong interest in contributing to Toronto's world-class AI ecosystem

If you're ready to build transformative ML systems with organizational backing, cutting-edge technology, and talented colleagues-this is the moment to join us.

Why Fitch?

At Fitch Group, the combined power of our global perspectives is what differentiates us. Our global network of colleagues comes together to accomplish things greater than they ever could alone.

Every team member is essential to our business, and each perspective is critical to our success. We embrace a diverse culture that encourages a free exchange of ideas, guaranteeing your voice will be heard and your work will have an impact, regardless of seniority.

We are building incredible things at Fitch and we invite you to join us on our journey.

About Fitch Group

Fitch Group is a global leader in financial information services with operations in more than 30 countries. Wholly owned by the Hearst Corporation, we are comprised of three main businesses: Fitch Ratings | Fitch Solutions | Fitch Learning.

For more information please visit our websites: www.fitchratings.com | www.fitchsolutions.com | www.fitchlearning.com


Fitch is committed to providing global securities markets with objective, timely, independent and forward-looking credit opinions. To protect Fitch's credibility and reputation, our employees must take every precaution to avoid conflicts of interests or any appearance of a conflict of interest. Should you be successful in the recruitment process at Fitch Ratings you will be asked to declare any securities holdings and other potential conflicts prior to commencing employment. If you, or your immediate family, have any holdings that may conflict with your work responsibilities, you may be asked to divest yourself of them before beginning work.

Fitch Group is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.

FOR TORONTO ROLES ONLY: Expected base pay rates for the role will be between 150,000 CAD and 200,000 CAD. Actual salaries will be determined on an individualized basis and may vary based on factors including but not limited to education, training, experience, past performance, and other job-related factors. Base pay is one part of Fitch's total compensation package, which, depending on the position, may also include commission earnings, discretionary bonuses, long-term incentives, and other benefits sponsored by Fitch.  

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