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    Machine learning engineer video - San Francisco, CA, United States - Gensyn

    Gensyn
    Gensyn San Francisco, CA, United States

    3 weeks ago

    Default job background
    Description

    Machine learning models are driving our cars , testing our eyesight , detecting our cancer , giving sight to the blind , giving speech to the mute , and dictating what we consume, enjoy, and think .


    Soon, we'll conjure unlimited content:

    from never-ending TV series (where we're the main character) to personalised tutors that are infinitely patient and leave no student behind.

    We'll augment our memories with foundation models —individually tailored to us through RLHF and connected directly to our thoughts via Brain-Machine Interfaces—blurring the lines between organic and machine intelligence and ushering in the next generation of human development.

    Gensyn is the machine learning compute protocol that translates machine learning compute into an always-on commodity resource—outside of centralised control and as ubiquitous as electricity—accelerating AI progress and ensuring that this revolutionary technology is accessible to all of humanity through a free market.

    Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs.

    Small team - misalignment and politics scale super-linearly with team size. Thin protocol - build and design thinly .

    Give direct feedback to everyone immediately rather than avoiding unpopularity , expecting things to improve naturally, or trading short-term pain for extreme long-term pain.

    Lower deep learning graphs - from common frameworks (PyTorch, Tensorflow, Keras, etc) down to an IR representation for training - with particular focus on ensuring reproducibility
    Front-end - deal with the handshaking of common Deep Learning Frameworks with Gensyn's IR for internal IR usage. Write Transformation passes in ONNX to alter IR for middle-end consumption
    Middle-end - write compiler passes for training-based compute graphs, integrate reproducible Deep Learning kernels into the code generation stage, and debug compilation passes and transformations as you go
    Back-end: lower IR from middle-end to GPU target machine code
    Compiler knowledge - base-level understanding of a traditional compiler (LLVM, GCC) and graph traversals required for writing code for such a compiler
    Solid software engineering skills - practicing software engineer, having significantly contributed to/shipped production code
    Low Level IR/LLVM targets/target-specific optimisations - particularly GPU specific optimisations
    Architecture understanding - full understanding of a computer architecture specialised for training NN graphs (Intel Xeon CPU, GPUs, TPUs, custom accelerators)
    Compilation understanding - strong understanding of compilation in regards to one or more High-Performance Computer architectures (CPU, GPU, custom accelerator, or a heterogenous system of all such components)
    Proven technical foundation - in CPU and GPU architectures, numeric libraries, and modular software design
    Deep Learning understanding - both in terms of recent architecture trends + fundamentals of how training works, and experience with machine learning frameworks and their internals (e.g. Exposure to a Deep Learning Compiler frameworks - e.g. Kernel Experience - Experience writing and optimizing highly-performant GPU kernels
    **Fully remote work - we hire between the West Coast (PT) and Central Europe (CET) time zones
    Whatever equipment you need
    Private health, vision, and dental insurance - including spouse/dependents [ only]
    #
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