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Senior Machine Learning Engineer - Hardware Abstractions & Performance Optimization
Posted 22 days agoViewed
💎 Seniority level: Senior
🔍 Industry: Machine Learning
🏢 Company: Luma AI👥 1-10💰 $43,000,000 Series B about 1 year agoVirtual RealityAugmented RealityArtificial Intelligence (AI)Computer VisionVideo Games3D Technology
Requirements:
- Experience optimizing for memory, latency and throughput in Pytorch.
- Experience using torch.compile / torch.XLA.
- Experience benchmarking and profiling GPU & CPU code in Pytorch for optimal device utilization (examples: torch profiler, memory profilers, trace viewers, custom tooling).
- Experience building tools & abstractions to ensure models run optimally on different hardware and software stacks .
- Experience working with transformer models and attention implementations.
- Experience with parallel inference, particularly with tensor parallelism, pipeline parallelism.
Responsibilities:
- Ensure efficient implementation of models & systems with a focus on designing, maintaining, and writing abstractions that scale beyond NVIDIA/CUDA hardware.
- Identify and remedy efficiency bottlenecks (memory, speed, utilization, communication) by profiling and implementing high-performance PyTorch code, deferring to Triton or similar kernel-level languages as necessary.
- Benchmarking our products across a variety of hardware & software to help the product team understand the optimal tradeoffs between latency, throughput and cost at various degrees of parallelism.
- Work together with our partners to help them identify bottlenecks and push forward new iterations of hardware and software.
- Work closely together with the rest of the research team to ensure systems are planned to be as efficient as possible from start to finish and raise potential issues for hardware integration.
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