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Graph Neural Network Jobs in Kentucky (NOW HIRING)

$140 - $210/hr

... neural network optimization Technical Skills * Programming Languages: Python and C (essential), Assembly * Compiler Frameworks: LLVM, MLIR, GCC, custom backend development * Graph Theory: Graph ...

$99 - $225/hr

Experience with computer vision, multimodal AI, geospatial analytics, graph neural networks, temporal or dynamic graph networks, network science, or social network analysis * Secret clearance AWS AI ...

New

$127 - $190/hr

Our inference engine empowers developers to deploy neural network models on Snapdragon platforms at ... Experience with optimizing software, specifically AI graph workloads, for embedded platforms*

$184 - $288/hr

... ML -- graph and equivariant neural networks, generative models, or surrogate modeling ... compute, networking, and orchestration. * Solid written and oral communication skills and ...

New

Graph Neural Network information

What is a graph neural network?

A Graph Neural Network (GNN) job typically involves designing, implementing, and optimizing neural network models that operate on graph-structured data. Professionals in this role apply GNNs to tasks like recommendation systems, fraud detection, social network analysis, and molecular property prediction. Responsibilities often include data preprocessing, model architecture selection, training, evaluation, and deployment. Strong knowledge of machine learning, deep learning frameworks (such as PyTorch or TensorFlow), and graph theory is essential.

What does a typical project workflow look like for a graph neural network engineer?

A typical project workflow for a Graph Neural Network Engineer involves collaborating with data scientists and domain experts to understand the problem, preprocessing and visualizing graph-structured data, and selecting appropriate model architectures. The role often includes building, training, and evaluating GNN models, iterating on hyperparameters, and deploying models to production environments. Throughout the process, you will engage in code reviews, document findings, and present results to stakeholders. Teamwork and effective communication are essential, as projects frequently require close collaboration with researchers, software engineers, and business units to ensure solutions meet practical needs and performance goals.

What are the key skills and qualifications needed to thrive in the graph neural network position, and why are they important?

To excel as a Graph Neural Network Engineer, you need a strong background in machine learning, graph theory, neural networks, and proficiency in programming languages such as Python. Familiarity with deep learning frameworks like PyTorch or TensorFlow, and experience with specialized libraries such as DGL or PyTorch Geometric are highly valued. Excellent problem-solving skills, teamwork, and the ability to communicate complex concepts to both technical and non-technical stakeholders will help you stand out. These combined abilities enable professionals to design, implement, and deploy cutting-edge GNN models that address complex, real-world data-structure challenges across various industries.

What are popular job titles related to Graph Neural Network jobs in Kentucky?

For Graph Neural Network jobs in Kentucky, the most frequently searched job titles are:

What cities in Kentucky are hiring for Graph Neural Network jobs?

Cities in Kentucky with the most Graph Neural Network job openings:

$140 - $210/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 4 days ago


Job description

About Neurophos

The demand for new data centers and AI compute is rapidly outpacing the planet's energy capacity. Digital solutions are hitting a power wall as we approach the physical limits of traditional silicon. Conquering this bottleneck means rethinking the fundamental architecture of inference compute. The industry's current path can't meet the need, so we're taking a different approach.

Instead of traditional electronic circuits, we use silicon photonics and an active, programmable metasurface to perform matrix multiplications at the speed of light. Our optical cells are 10,000x smaller than traditional photonic components, enabling unprecedented density. By using photonics instead of electricity, our chips become more efficient as they scale. This architecture will deliver up to 100 times the energy efficiency of existing solutions while significantly improving performance for large-scale AI inference.

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.

Join us and shape the future of computing!

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.

Location: San Jose, CA or Austin, TX. Full-time onsite position.

Key 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

Qualifications:

  • Master's degree in Computer Science or related field

  • 5+ years of experience in 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

Preferred Skills:

  • PhD in Computer Science or related field

  • 7+ years of industry experience

  • 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

Technical Skills

  • Programming Languages: Python and C (essential), Assembly

  • Compiler Frameworks: LLVM, MLIR, GCC, custom backend development

  • Graph Theory: Graph algorithms, graph rewriting systems, DAG optimization

  • AST Processing: Parsing, analysis, and transformation of abstract syntax trees

  • Testing & QA: pytest, GoogleTest, or similar frameworks; static analysis tools

  • CI/CD: Jenkins, GitHub Actions, GitLab CI, or similar systems

  • LLM Technologies: Transformer architectures, attention mechanisms, quantization techniques

  • Development Tools: CMake, Git, Docker

  • Parallel Tools: Profilers, debuggers, and instrumentation for parallel/concurrent programs

Technical Environment

  • Languages: Python and C (primary), Assembly for low-level optimization

  • Compiler Tools: LLVM, MLIR, GCC, custom compiler backends

  • Testing: Automated test suites, continuous integration pipelines

  • Frameworks: PyTorch/JAX/Triton integration, custom inference engines

  • Focus Areas: Compiler backend development, optimization passes, hardware-software co-design

What We Offer

This is an opportunity to play a pivotal role in an innovative startup redefining the future of AI hardware. Work on game-changing technology at the intersection of photonics and AI as part of a collaborative, brilliant team. You’ll contribute to a platform that redefines computational performance and accelerates the future of artificial intelligence. Come help us bring this transformative technology to the world.

Benefits

Join a team that invests in your future and your well-being. At Neurophos, we offer:

  • 100% coverage of base health plan premiums for you and your dependents, plus HSA contributions.

  • Unlimited PTO. No rigid vacation banks, just a focus on delivery.

  • 401(k) matching and stock option opportunities to ensure our success is your success.

  • Full suite of voluntary benefits, including Dental, Vision, Life, Hospital, Critical Illness, and Accident insurance.

  • Personalized Benefits. Choose the plans that fit your life and take the cash back for those that don’t.

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