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    Machine Learning Systems Engineer - San Francisco, CA, United States - Gensyn

    Gensyn
    Gensyn San Francisco, CA, United States

    3 weeks ago

    Default job background
    Description

    The world will be unrecognisable in 5 years.

    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 . These AI systems are already an integral part of our lives and will shape our future as a species.

    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.

    This future demands immense, globally accessible, uncensorable, computational power. 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.

    Our Principles:

    Autonomy

    • Don't ask for permission - we have a constraint culture , not a permission culture.
    • Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs.
    • Push & pull context on your work rather than waiting for information from others and assuming people know what you're doing.
    • No middle managers - we don't (and will likely never) have middle managers.

    Focus

    • Small team - misalignment and politics scale super-linearly with team size. Small protocol teams rival much larger traditional teams.
    • Thin protocol - build and design thinly .
    • Reject waste - guard the company's time, rather than wasting it in meetings without clear purpose/focus, or bikeshedding .

    Reject Mediocrity

    • 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.
    • Embrace an extreme learning rate rather than assuming limits to your ability/knowledge.

    Responsibilities:

    Build execution frameworks - design and create low-level execution frameworks to train models on remote hardware

    Scale - build methods for handling datasets, models and training procedures across all shapes/sizes

    Design and own your code - take care of designing the frameworks and handle all maintenance related to keeping your codebase up to date

    Follow best practices - build in the open with a keen focus on designing, testing, and documenting your code

    Write & engage - contribute to technical reports/papers describing the system and discuss with the community

    Minimum Requirements:

    Framework development - experience interfacing with common ML Frameworks (Tensorflow, PyTorch, Onnx, etc) for your own abstractions

    Distributed Systems Experience - experience working with many compute resources for large computations

    Solving Problems that Scale - experience with working on Massive datasets/Large Language Models. Knowledge of Pipeline and Data Parallelism Techniques

    Infrastructure - previously provisioned and/or configured large-scale infrastructure for ML training tasks

    Background in relevant theory - i.e. computer science, machine learning, algorithms, distributed systems

    Strong willingness to learn Rust - as a Rust by default company, we require that everyone learns Rust so that they have context/can work across the entire codebase

    Highly self-motivated with excellent verbal and written communication skills

    Comfortable working in an applied research environment - with extremely high autonomy

    Nice to haves:

    Rust experience - systems level programming experience in Rust

    Compiler-level experience - previously worked at the lower levels of the ML stack - on compilers or hardware implementation

    Quantisation / constrained optimisation - previously worked on overcoming bottlenecks in ML training from hardware or communication constraints

    Decentralised model training frameworks - exposure to frameworks like Moshpit SGD that effectively optimise models despite high latency, heterogeneous, compute environments

    Open source work - experience working with large open source codebases (ideally as maintainer)

    Compensation & Benefits:

    Competitive salary + share of equity and token pool

    Fully remote work - we hire between the West Coast (PT) and Central Europe (CET) time zones

    4x all expenses paid company retreats around the world, per year

    Whatever equipment you need

    Paid sick leave

    Private health, vision, and dental insurance - including spouse/dependents [ only]

    #J-18808-Ljbffr
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