Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
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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