Machine Learning Engineer - Toronto - Altera

    Altera
    Altera Toronto

    6 days ago

    Description
    Altera .

    Machine Learning Engineer page is loaded## Machine Learning Engineerlocations:
    Toronto, Ontario, Canadatime type: Full timeposted on: Posted Yesterdayjob requisition id: R01911#

    Job Details:    ### ##

    Job Description:Altera is a global leader in programmable logic solutions, delivering high performance FPGA technology that powers next generation cloud, networking, and edge applications.

    With a renewed focus on agility, software first usability, and hardware accelerated innovation, Altera is shaping the future of computing.

    Join us as we build the next wave of AI optimized FPGA platforms and tools
    About the RoleWe are seeking a Machine Learning Engineer to help drive the development, optimization and deployment of Altera FPGA Compiler.

    In this role, you will work at the intersection of
    machine learning and compiler/toolchain development, enabling customers to achieve breakthrough performance and efficiency on programmable logic.

    You will collaborate closely with hardware architects, software engineers, and IP developers to design ML models, optimize inference pipelines, and contribute to the evolution of Altera's FPGA Compiler.


    Key Responsibilities Develop, optimize, and deploy advanced machine learning technologies to enhance FPGA compiler performance, focusing on timing closure, resource utilization, and power efficiency.
    • Evaluate and integrate emerging ML models (e.g., graph neural networks, reinforcement learning) and frameworks (e.g., PyTorch, TensorFlow) for compiler optimization tasks like placement, routing, and logic synthesis.
    • Build robust tools, scripts, and CI/CD workflows to automate model conversion, quantization, pruning, and deployment within the FPGA design flow, ensuring compatibility with EDA tools like Quartus.
    • Collaborate with customers, FPGA architects, and internal engineering teams to gather ML requirements, define success metrics, and deliver tailored, production-ready solutions.
    • Design and execute comprehensive benchmarks to measure ML-optimized compiler performance across diverse FPGA families (e.g., Agilex, Stratix), analyzing metrics such as compile time, QoR, and scalability.
    Our compensation is designed to reflect the Canadian labour market.

    The actual salary offered may vary based on several factors, including the position's location, as well as the candidate's experience, skills, training, and job-specific knowledge.

    In addition to base salary, we offer performance-based incentive opportunities that reward both individual contributions and overall company success.


    Estimated Salary Range:
    $172.1 – $253.7CADWe use artificial intelligence to screen, assess, or select applicants for the position. This posting is for an existing vacancy. Canadian work experience is not required for this role. Applicants must be eligible for any required Canada export authorizations.

    ### ##
    Qualifications:

    *Minimum Qualifications
    Bachelor's Degree or higher in Computer Science, Electrical Engineering, or a related field. 10+ years of experience in machine learning development, model optimization or ML systems engineering. Experience with C++ and Python in production or research environments.Preferred Qualifications
    Experience with Agile methodologies, GitHub Copilot or similar AI coding assistants, and high-performance computing environments. Familiarity with edge AI inference on FPGAs and neuro-symbolic AI techniques. Strong communication skills for cross-functional collaboration and presenting results at conferences like DAC or FPGA World.

    ### ##     Job Type:


    Regular### ## Shift:

    Shift 1 (Canada)### ##

    Primary Location:

    Toronto, Ontario, Canada### ##

    Additional Locations:

    ### ##

    Posting Statement:
    **All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
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