Compiler Engineer - Distributed Machine Learning

Gensyn; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

A leading company in machine intelligence seeks a Compiler Developer to enhance deep learning frameworks. Responsibilities include writing algorithms and owning compiler areas. This fully remote position offers a competitive salary, equity, and a flexible work environment.

Benefits

Competitive salary + share of equity and token pool

Fully remote work

Visa sponsorship available

Paid sick leave and flexible vacation

Company-sponsored health, vision, and dental insurance

3-4x all expenses paid company retreats

Whatever equipment you need

Qualifications

• Understanding of traditional compilers (LLVM, GCC) and graph traversals.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
• Ability to work in an applied research environment with high autonomy.

Responsibilities

• Write novel algorithms for transforming compute graph representations.
• Own areas in compiler development: front-end, middle-end, or back-end.
• Integrate reproducible deep learning kernels into code generation.

Skills

Compiler knowledge

Solid software engineering skills

Understanding of parallel programming

High-Level IR/Clang/LLVM knowledge

Low Level IR/LLVM targets knowledge

Excellent communication skills

Self-motivated

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

• Lowering deep learning graphs - from common frameworks (PyTorch, Tensorflow, Keras, etc) down to an IR for training and inference - with particular focus on ensuring reproducibility

Responsibilities

• Write novel algorithms - for transforming intermediate representations of compute graphs between different operator representations
• Own two of the following compiler areas:
  • 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

Must have

• 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
• Understanding of parallel programming - specifically as it pertains to GPUs
• Ability to operate on:
• High-Level IR/Clang/LLVM up to middle-end optimisation; and/or
• Low Level IR/LLVM targets/target-specific optimisations - particularly GPU specific optimisations
• Highly self-motivated with excellent verbal and written communication skills
• Comfortable working in an applied research environmentwith extremely high autonomy

Preferred

• 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. PyTorch, TensorFlow, scikit-learn, etc.)
• Exposure to a Deep Learning Compiler frameworks - e.g. TVM, MLIR, TensorComprehensions, Triton, JAX
• Kernel Experience - Experience writing and optimizing highly-performant GPU kernels

Nice to have

• Open-source contributions to existing compilers/frameworks with a strong preference for ML compilers/frameworks.
• 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]
• 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

• 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 .

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