Gensyn
GPU Software Engineer
Job Summary
This role involves implementing core GPU components for distributed machine learning training within a decentralized protocol. The candidate should have strong software engineering skills and experience with GPU kernels, distributed compute environments, and deep learning frameworks like PyTorch or TensorFlow. Responsibilities include developing performant GPU compute infrastructure, designing numerical algorithms, and working with hardware architectures. The position offers remote work options, competitive compensation, and benefits such as health insurance and company retreats.
Required Skills
Benefits
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 currently hire between the West Coast (PT) and Central Europe (CET) time zones
- Visa sponsorship - available for those who would like to relocate to the US after being hired
- 3-4x all expenses paid company retreats around the world, per year
- Whatever equipment you need
- Paid sick leave and flexible vacation
- Company-sponsored 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 and design thinly.
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