Machine Learning Engineering Intern developing innovative AI solutions for contact centers at Cresta. Collaborating with top researchers and engineers in AI to unlock customer experience potential.
NV
Machine Learning Applications and Compiler Engineer – New College Grad
Posted 2 months ago
About the role
- Engineer developing algorithms and optimizations for NVIDIA's LPX inference and compiler stack. Collaborating on neural network workload mappings to NVIDIA platforms.
Responsibilities
- Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.
- Define and implement mappings of large-scale inference workloads onto NVIDIA’s systems.
- Extend and integrate with NVIDIA’s SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.
- Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.
- Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.
- Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.
- Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.
Requirements
- Pursuing or recently completed a MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience.
- Possess software engineering background with familiarity in systems level programming (e.g., C/C++ and/or Rust) and solid CS fundamentals in data structures, algorithms, and concurrency.
- Hands on experience with compiler or runtime development, including IR design, optimization passes, or code generation.
- Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.
- Familiarity with deep learning frameworks such as TensorFlow and PyTorch, and experience working with portable graph formats such as ONNX.
- Understanding of parallel and heterogeneous compute architectures, such as GPUs, spatial accelerators, or other domain specific processors.
- Strong analytical and debugging skills, with experience using profiling, tracing, and benchmarking tools to drive performance improvements.
- Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.
- Ideal candidates will have direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.
Benefits
- equity
- benefits
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