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Machine Learning Software Engineer Intern Jobs in Brampton, ON

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... Knowledge of software engineering best practices including version control (Git) and CI/CD ...

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... Knowledge of software engineering best practices including version control (Git) and CI/CD ...

Omers

Lead, AI/Machine Learning Engineer

Toronto, ON · On-site

Collaborating with Software Engineering, Customer Success, and business stakeholders in an Agile environment to move initiatives from prototype to production and ensure successful adoption.

Serve Robotics

Lead Machine Learning Engineer

Toronto, ON · Remote

$225K - $260K/yr

Who We Are We are tech industry veterans in software, hardware, and design who are pooling our ... Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a ...

Advanced Micro Devices, Inc

We are seeking highly motivated Software Engineering intern/co-op to join our team at AMD. In this role - We will assign you responsibilities in which you will work closely with our software ...

Advanced Micro Devices, Inc

We are seeking highly motivated Software Engineering intern/co-op to join our team at AMD. In this role * We will assign you responsibilities in which you will work closely with our software ...

YO AI Labs

Machine Learning Engineer - Remote

Toronto, ON · Remote

$80 - $120/hr

Senior Software Engineer Job Type: Contractor (~15 hours/week) Location: Remote Job Summary We are ... Create reinforcement learning environments for software engineering tasks. * Design tasks involving ...

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Machine Learning Software Engineer Intern information

What does a machine learning software engineer intern do?

A Machine Learning Software Engineer Intern assists in the development, testing, and deployment of machine learning models and algorithms. Their responsibilities typically include data preprocessing, model training, evaluation, and collaborating with senior engineers to integrate machine learning solutions into software products. Interns may also contribute to research, documentation, and code optimization, gaining hands-on experience with real-world machine learning projects. This role provides a valuable opportunity to apply academic knowledge in a professional setting and learn from experienced engineers.

What are the key skills and qualifications needed to thrive as a machine learning software engineer intern?

To thrive as a Machine Learning Software Engineer Intern, you need a solid understanding of programming (especially Python), machine learning algorithms, and data structures, ideally supported by coursework or relevant projects. Familiarity with frameworks such as TensorFlow or PyTorch, experience using version control systems like Git, and knowledge of cloud platforms are highly valuable. Critical thinking, eagerness to learn, and effective communication help interns collaborate with teams and adapt to new challenges. These skills and qualities are crucial for developing robust ML solutions, integrating with production systems, and contributing meaningfully to real-world projects.

What cities near Brampton, ON are hiring for Machine Learning Software Engineer Intern jobs?

Cities near Brampton, ON with the most Machine Learning Software Engineer Intern job openings:

Machine Learning Software Engineer Intern jobs near you

Senior Machine Learning Engineer

Toronto, ON • Remote

Career Renew
See more jobs at Career Renewexternal

$165K - $225K/yr

Full-time

Re-posted 20 days ago

Job description

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus equity.
 
We are the leading virtual staining company revolutionizing digital pathology adoption worldwide through cutting-edge AI-powered technology. Our solutions deliver diagnostic-quality results in minutes while preserving tissue samples for comprehensive analysis.
Our breakthrough DeepStain™ and ReStain™ technologies enable unlimited virtual staining from a single tissue sample, eliminating the bottlenecks and limitations of traditional chemical staining processes. This innovation supports the critical evolution from research applications to clinical deployment, empowering laboratories to advance their digital pathology capabilities while reducing chemical waste, improving operational efficiency, and expanding diagnostic possibilities.

About the Role

We are seeking an experienced Senior ML Engineer to join our team who owns the representation-learning and generative modeling stack that powers Pictor’s virtual staining. The ideal candidate will have deep expertise in Machine Learning and building generalizable, production-ready models, and evaluations that stand up in clinical workflows.
      Design and implement novel computer vision and deep learning algorithms for virtual staining and digital pathology applications
      Conduct rigorous experiments to evaluate algorithm performance, validate research hypotheses, and drive iterative improvements
      Develop and advance ML models leveraging Vision Transformers, Diffusion Models, GANs, and generative architectures for image-to-image translation tasks
      Apply classical and learned image enhancement, denoising, and semantic segmentation techniques to histopathology imaging challenges
      Explore image representation in latent space for efficient, high-fidelity virtual staining
      Stay current with state-of-the-art research, identifying opportunities to apply novel techniques to PictorLabs’ product roadmap

Collaboration
      Collaborate with ML Engineering and software teams to translate research prototypes into production-ready systems meeting latency and throughput requirements
      Work with large-scale pathology datasets to train, validate, and fine-tune foundation models and custom architectures
      Partner with software engineers, data scientists, and pathology domain experts to integrate research into production systems
      Contribute to best practices for data engineering, data governance, and data quality across research and production pipelines
      Leverage AI coding and ideation tools to accelerate research velocity and prototype new approaches

Required Qualifications

      PhD (preferred) or Master’s degree in Computer Science, Electrical Engineering, or a related field
      Deep expertise in computer vision and deep learning, with hands-on experience in one or more of: Vision Transformers, Diffusion Models, GANs, semantic segmentation, or classical image enhancement and denoising
      Expert proficiency in Python and PyTorch and other scientific computing environments a plus
      Strong mathematical foundation in linear algebra, probability, and optimization
      Experience with large-scale model training, distributed computing, or cloud ML infrastructure (AWS, GCP, or Azure)
      Knowledge of handling large scale image data,  data version controls,  model registry, has experience dealing with ML lifecycles
      Experience with feature search, data balancing, and data curation pipelines.
      Knowledge of software engineering best practices including version control (Git) and CI/CD pipelines
      Excellent collaboration and communication skills, with the ability to work effectively in a fast-paced, cross-functional international startup environment
      Extensive use of AI tools for coding, optimization, and ideation

Preferred Qualifications

      Experience with medical imaging, digital pathology, or whole slide image (WSI) processing
      Experience with LoRAs,  transformer architecture and state of the art image to image translation models (Flux 2, Z-Image) and the Hugging face ecosystem
      Background in generative models and fine-tuning of foundation models
      Experience with GPU acceleration and optimization, including CUDA kernel engineering, TensorRT/ONNX export, and inference serving frameworks such as Triton
      Experience with hosting  computer vision model inference on NVIDIA DGX Spark.
      Understanding of FDA regulatory requirements for AI/ML in medical devices
      Experience with MLOps tools (MLflow, Kubeflow) and model versioning practices
      Develop tools and frameworks to streamline ML research workflows, experimentation, and reproducibility

What We Offer

The opportunity to work on technology that directly improves patient outcomes and transforms clinical diagnostics, alongside a talented team of engineers and researchers pushing the boundaries of AI in healthcare. You will have the freedom to pursue high-impact research while seeing your work deployed at scale in real clinical environments.