Offer summary

Qualifications:

Strong software engineering skills with experience in production-level code., Hands-on experience in distributed GPU compute environments, including writing GPU kernels., In-depth understanding of deep learning and experience with machine learning frameworks like PyTorch and TensorFlow., Preferred knowledge of heterogeneous system architecture and compiler technology..

Key responsabilities:

Develop performant GPU kernels and compute infrastructure for distributed ML training.

Design novel algorithms focusing on numerical properties and stable compute flows.

Implement low-level GPU-specific optimizations for performance and numerical stability.

Ensure reproducibility in multi-GPU distributed training environments.

Job description

Machine intelligence will soon take over humanity’s role in knowledge-keeping and creation. What started in the mid-1990s as the gradual off-loading of knowledge and decision making to search engines will be rapidly replaced by vast neural networks - with all knowledge compressed into their artificial neurons. Unlike organic life, machine intelligence, built within silicon, needs protocols to coordinate and grow. And, like nature, these protocols should be open, permissionless, and neutral. Starting with compute hardware, the Gensyn protocol networks together the core resources required for machine intelligence to flourish alongside human intelligence.

The Role

Implement core GPU components for distributed ML training wrapped in a decentralised protocol

Responsibilities

Develop performant GPU kernels and compute infrastructure - from the framework level (e.g. PyTorch) down to IR for training, with a strong emphasis on reproducibility in multi-GPU distributed training environments
Design novel algorithms - with a focus on numerical properties and stable compute flows, optimised for modern cryptographic systems

Competencies

Must have

Strong software engineering skills - with substantial experience as a practising software engineer and significant contributions to shipping production-level code
Hands on experience in distributed GPU compute environments:

Writing GPU Kernels (e.g. CUDA, PTX, MPX/MLX, IR); and/or
Implementing low-level GPU-specific optimizations for performance, numerical stability and determinism

In-depth understanding of deep learning - including recent architectural trends, training fundamentals, and practical experience with machine learning frameworks and their internal mechanics (e.g., PyTorch, TensorFlow, JAX)

Preferred

Deep understanding of heterogenous system architecture
Experience in a venture backed start-up environment

Nice to have

Open-source contributions to high-performance GPU codebases
Strong understanding of computer architecture - with expertise in specialised architectures for training neural networks, including Intel Xeon CPUs, GPUs, TPUs, and custom accelerators, as well as heterogeneous systems combining these components
Solid foundation in compiler technology - with a working knowledge of traditional compilers (e.g., LLVM, GCC) and graph traversal algorithms
Experience with deep learning compiler frameworks - such as TVM, MLIR, TensorComprehensions, Triton, and JAX
Experience working with distributed training infrastructure and software development

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
Relocation Assistance - available for those that would like to relocate after being hired (anywhere from PST through CET time zones)
3-4x 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]

Our Principles

Autonomy & Independence

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.
Communicate to be understood rather than pushing out information and expecting others to work to understand it.
Stay a small team - misalignment and politics scale super-linearly with team size. Small protocol teams rival much larger traditional teams.

Rejection of mediocrity & high performance

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.
Don’t quit - push to the final outcome, despite any barriers.
Be anti-fragile - balance short-term risk for long-term outcomes.
Reject waste - guard the company’s time, rather than wasting it in meetings without clear purpose/focus, or bikeshedding.
Build thinly - build and design thinly.

Required profile