Lead Compiler Engineer

Neurophos • Austin, TX

6h • Onsite

About The Position

We are developing an ultra-high-performance, energy-efficient photonic AI inference system. We’re transforming AI computation with the first-ever metamaterial-based optical processing unit (OPU).
As AI adoption accelerates, data centers face significant power and scalability challenges. Traditional solutions are struggling to keep up, leading to rapidly rising energy consumption and costs. We’re solving both problems with an OPU that integrates over one million micron-scale optical processing components on a single chip. This architecture will deliver up to 100 times the energy efficiency of existing solutions while significantly improving large-scale AI inference performance.
We’ve assembled a world-class team of industry veterans and recently raised a $110M Series A led by Gates Frontier. Participants include M12 (Microsoft’s Venture Fund), Carbon Direct Capital, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others. We have also been recognized on the EE Times Silicon 100 list for several consecutive years.
Join us and shape the future of optical computing!
Location: Austin, TX. Full-time onsite position.
Position Overview:
We are seeking a talented ML Compiler Engineer to join our engineering team and lead the development of our compiler. This role focuses on compiler development for our novel LLM accelerator architecture. This is one of several software stacks that seamlessly bridge high-level AI workloads with our custom hybrid optical-electronic compute hardware, enabling customers to realize game-changing performance.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, or related field
• 10+ years of industry experience
• 5+ years of professional experience in systems programming or compiler development
• Expert-level proficiency in Python and C
• Experience with hardware compilers
• Familiarity with Large Language Model architectures and their computational requirements
• Hands-on experience with compiler frameworks and code optimization techniques
• Deep understanding of computer architecture, memory hierarchies, and parallel computing concepts
• Experience with AI/ML accelerators (GPUs, TPUs, FPGAs) and their programming models

Nice To Haves

• Master's degree in Computer Science, Computer Engineering, or related field
• Strong background in graph theory and graph transformations in a compiler or optimization context; MLIR experience is a plus
• Experience writing programs that parse, analyze, and mutate programs as abstract syntax trees
• Experience in instrumenting and debugging parallel programs
• Experience with structured, human-supervised AI/agentic coding workflows
• Experience with LLM quantization techniques and model optimization
• Experience with high-performance computing and low-latency system design
• Familiarity with deep learning frameworks and neural network optimization

Responsibilities

• Design and implement toolchains for our custom LLM accelerator architecture
• Develop optimization strategies that bridge software algorithms to hardware implementations
• Design and implement custom compiler components, including IR dialects, graph transformations, and lowering passes
• Optimize computational graphs and memory access patterns for our hardware architecture
• Integrate with existing ML frameworks (e.g., PyTorch, JAX, Triton).
• Build and maintain test infrastructure to ensure compiler correctness and performance

Benefits

• A pivotal role in an innovative startup redefining the future of AI hardware.
• A collaborative and intellectually stimulating work environment.
• Competitive compensation package including equity participation.
• Comprehensive benefits, including health, dental, and vision insurance.
• Opportunities for career growth and future team leadership.
• Access to cutting-edge technology and state-of-the-art facilities.
• Opportunity to publish research and contribute to the field of efficient AI inference.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level