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Parallel Computing Jobs in Ontario (NOW HIRING)

Nasdaq, Inc.

Senior Software Engineer

Toronto, ON · On-site

$171.48 - $240.93/hr

Familiarity with parallel computing or stream processing frameworks. * Experience working in high‑volume, low‑latency, and highly available system environments. Benefits We offer a competitive ...

Nasdaq

Senior Software Engineer

Toronto, ON · Hybrid

CA$121K - CA$170K/yr

Familiarity with parallel computing or stream processingframeworks. * Experience working in high-volume, low-latency, and highly available system environments. This position will be located in ...

Waabi

Senior / Staff Software Engineer

Toronto, ON · On-site

... parallel computing environments, emulation at scale, software performance, optimization, and profiling, concurrency and determinism, test-driven and API-driven development methodologies, system ...

Waabi

Research Engineer, Sensor Signal Processing

Toronto, ON · On-site +1

CA$155K - CA$269K/yr

... parallel computing architectures (e.g., CPU, GPU, DSP, and specialized accelerators). - Collaborate with Waabi's autonomy and hardware teams to ensure the robustness of the entire system. - Have the ...

Alquemy

Senior .NET Developer

Toronto, ON · On-site

... computing, building the backbone for complex derivative valuation and risk management This is a ... Implement advanced parallel and asynchronous programming patterns (TPL, Multi-threading) to process ...

Altera

FPGA Compiler (Router) Engineer

Toronto, ON

CA$115/hr

Familiarity with parallel/distributed computing for EDA workloads * Experience with scripting (Python,Tcl) for tooling and automation * Background in timing analysis or placement algorithms Job Type:

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Parallel Computing information

See Ontario salary details

$23K

$111K

$193.5K

How much do parallel computing jobs pay per year?

As of Jul 11, 2026, the average yearly pay for parallel computing in Ontario is $111,031.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $140,000.00 per year, depending on experience, location, and employer.

Is parallel computing difficult?

Parallel computing as a job involves designing and implementing systems that perform multiple tasks simultaneously, which requires strong problem-solving skills, knowledge of algorithms, and proficiency with programming tools like MPI or OpenMP. The difficulty depends on the complexity of projects and the individual's experience, but mastering parallel algorithms and debugging concurrent processes can be challenging for beginners. Continuous learning and practical experience are essential for success in this field.

What are the key skills and qualifications needed to thrive as a Parallel Computing Specialist, and why are they important?

To thrive as a Parallel Computing Specialist, you need strong knowledge of computer architecture, parallel algorithms, and experience with programming languages such as C/C++, Python, and frameworks like MPI or OpenMP, often supported by a degree in computer science or a related field. Familiarity with high-performance computing (HPC) environments, GPU programming (CUDA, OpenCL), and cloud-based parallel processing systems is typically required. Analytical thinking, problem-solving abilities, and effective collaboration are crucial soft skills in this role. These skills are vital for efficiently designing, optimizing, and implementing solutions that leverage parallelism to significantly accelerate computational tasks.

What is the highest paying job in computing?

In computing, roles such as Chief Technology Officer (CTO), Solutions Architect, and Data Science Director tend to be among the highest paying, often earning six-figure salaries. Specialized skills in areas like artificial intelligence, cybersecurity, and cloud computing can also command top compensation levels for experienced professionals.

What are some common challenges faced by professionals working in parallel computing roles?

Professionals in parallel computing often encounter challenges such as efficiently dividing complex tasks among multiple processors and minimizing communication overhead between them. Debugging and optimizing performance across parallel architectures can be difficult, as issues like race conditions and load imbalances frequently arise. Additionally, staying current with evolving hardware technologies and parallel programming frameworks is essential to ensure solutions remain efficient and scalable. Collaborating with cross-functional teams, such as data scientists and system architects, is also crucial for integrating parallel solutions into larger projects.

What is the difference between Parallel Computing vs Data Analyst?

AspectParallel ComputingData Analyst
Required CredentialsComputer Science or Engineering degree, programming skillsStatistics, Data Science, or related degree, analytical skills
Work EnvironmentResearch labs, tech companies, high-performance computing centersBusiness, finance, healthcare, corporate offices
Industry UsageTechnology, research, scientific computingBusiness intelligence, market analysis, reporting

While Parallel Computing focuses on developing algorithms to process large data sets efficiently across multiple processors, Data Analysts interpret data to provide actionable insights. Both roles require strong technical skills but serve different purposes: one enhances computational performance, the other informs business decisions.

What is parallel computing?

Parallel computing is a type of computation where many calculations or processes are carried out simultaneously, leveraging multiple processors or computers to solve complex problems more efficiently. It divides large tasks into smaller ones that can be executed concurrently, significantly speeding up processing time. Commonly used in scientific research, data analysis, and engineering, parallel computing is essential for handling large-scale simulations and big data applications.

What engineers make $500,000?

Senior engineers in fields such as software, aerospace, or petroleum engineering can earn $500,000 or more annually, often through a combination of base salary, bonuses, and stock options. High compensation typically requires extensive experience, advanced skills, and working in high-demand industries or leadership roles.

What is an example of parallel computing in real life?

Parallel computing in a job context involves tasks like processing large datasets or simulations simultaneously across multiple processors or cores to improve efficiency. For example, data analysts may use parallel computing tools to analyze big data sets quickly, requiring knowledge of programming languages such as Python or C++ and familiarity with parallel processing frameworks like MPI or OpenMP.
What are popular job titles related to Parallel Computing jobs in Ontario? For Parallel Computing jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Parallel Computing jobs in Ontario look for? The top searched job categories for Parallel Computing jobs in Ontario are:
Parallel Computing jobs near you

Senior Software Developer - Numerical

Seequent

Toronto, ON

Other

Posted 2 days ago

Job description

The Role

Reporting to the Director, Software Development (or delegate), we are seeking an experienced Senior Software Developer - Numerical to join our geoscience software development team. This role is based in Toronto, ON, operating under a hybrid work model.

This position focuses on transforming early-stage research prototypes into high-quality, production-ready numerical software. You will design, implement, and optimize high-performance numerical libraries and compute kernels using C++, Python, and CUDA, ensuring scalability, robustness, and performance.

Working closely with researchers and domain experts, this role bridges scientific research and software engineering, requiring strong numerical expertise, performance-focused thinking, and effective cross-functional collaboration.

In this role, you will have the opportunity to

Numerical Software Development

  • Develop and optimize high-performance numerical software in C++, Python, and CUDA.
  • Re-engineer research prototypes into scalable, production-ready solutions.
  • Implement parallel programming techniques including multi-threading, vectorization, and GPU acceleration.
  • Ensure solutions are portable across Linux (primary) and Windows environments.

Performance & Quality

  • Profile, benchmark, and optimize numerical workflows using tools such as Nsight and VTune.
  • Improve runtime efficiency, memory usage, and algorithmic scalability.
  • Design automated testing and verification frameworks to ensure numerical correctness and reliability.

Collaboration & Engineering Excellence

  • Work closely with researchers, engineers, and product teams to translate research concepts into stable implementations.
  • Participate in design reviews, technical planning, and peer code reviews.
  • Produce clear technical documentation and apply modern engineering practices, including CI/CD (GitHub Actions) and version control.

Essential Knowledge, Skills, and Experience

  • Bachelor's degree in Computer Science, Applied Mathematics, Physics, or a related STEM field.
  • 7+ years of experience in software development or engineering roles.
  • Strong experience in numerical computing with C++ and Python.
  • Hands-on experience with CUDA, GPU programming, and parallel computing.
  • Knowledge of numerical libraries such as Eigen or BLAS.
  • Experience developing on Linux and supporting cross-platform environments.
  • Familiarity with performance profiling tools and CI/CD pipelines.

Assets

  • Experience with OpenMP, Rust, Fortran, cloud computing (GCP), containerization, or distributed systems.

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