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Parallel Computing Jobs in Ontario (NOW HIRING)

Arcadion

HPC Specialist

Ottawa, ON · On-site

... Parallel Computing MPI, OpenMP, CUDA Monitoring/Telemetry Prometheus, Grafana, Ganglia Cloud HPC AWS HPC, Azure CycleCloud, GCP HPC Toolkit Containers Singularity, Apptainer, Docker, Kubernetes AI/ML ...

Talentlab

GPU Compiler Engineer

Markham, ON

Parallel computing and highly parallel architectures. * Low-level systems programming. * Graphics APIs or compute environments. For more senior positions, we're interested in engineers with deep ...

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 ...

Tenstorrent

Software Engineer, TT-Distributed

Toronto, ON · On-site

CA$100K - CA$500K/yr

Enthusiastic about distributed computing, including IPC, socket programming, and cluster resource ... Design and build distributed APIs enabling data-parallel and tensor-parallel AI workloads.

Waabi

Research Engineer

Toronto, ON · On-site +1

CA$122K - CA$215K/yr

Solid experience with concurrent, parallel, and distributed computing techniques for efficient computation. * Proficiency in Pytorch, Rust, C++ and/or CUDA. The US yearly salary range for this role ...

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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 Sep 13, 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.

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 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 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 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.

Is parallel computing hard?

Parallel computing as a job involves designing and managing systems that perform multiple calculations 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 tasks and the level of expertise, but it generally involves understanding concurrency, synchronization, and performance optimization. Gaining experience through coursework, certifications, and hands-on projects can help reduce the learning curve.

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
Infographic showing various Parallel Computing job openings in Ontario as of September 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $111,031 per year, or $53.4 per hour.

Senior Software Developer - Numerical

Toronto, ON • On-site

Seequent
See more jobs at Seequentexternal

Full-time

Re-posted 6 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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