[Hiring] Senior Machine Learning Engineer - Hardware Abstractions & Performance Optimization @L[...]

Luma Ai; United States; Remote; USD 80,000 - 100,000; Full time

Job summary

An innovative firm is seeking a Senior Machine Learning Engineer to optimize performance across multiple hardware platforms. This role involves designing efficient systems and writing abstractions that maximize performance while working with cutting-edge multimodal AI technologies. You will collaborate with partners to identify bottlenecks and enhance product performance, ensuring that our state-of-the-art models operate at their best. Join a team dedicated to pushing the boundaries of AI and making a significant impact in the field of machine learning. If you are passionate about optimizing systems and have a knack for high-performance computing, this opportunity is perfect for you.

Qualifications

• Significant experience in optimizing memory, latency, and throughput in Pytorch.
• Experience with benchmarking and profiling GPU & CPU code for optimal utilization.

Responsibilities

• Ensure efficient implementation of models with a focus on performance optimization.
• Identify and remedy efficiency bottlenecks by profiling and implementing high-performance code.

Skills

Optimizing memory

Latency optimization

Throughput optimization

Pytorch

Benchmarking

Profiling GPU & CPU code

Transformer models

Parallel inference

Tools

torch.compile

torch.XLA

Triton

CUDA

C++

Gradio

Docker

Job description

Mar 22, 2025 - Luma Ai is hiring a remote Senior Machine Learning Engineer - Hardware Abstractions & Performance Optimization. Location: USA.

Luma’s mission is to build multimodal AI to expand human imagination and capabilities. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.

We are looking for engineers with significant experience maintaining & designing highly efficient systems and code that can be optimized to run on multiple hardware platforms, bringing our state-of-the-art models to as many people at the best performance per dollar.

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.
• Benchmark 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 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.

Must have experience

• Experience optimizing for memory, latency and throughput in Pytorch.
  • Bonus: experience with non-NVIDIA systems.
• 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 and pipeline parallelism.

Good to have experience

• Experience with high-performance Triton/CUDA and writing custom PyTorch kernels and ops. Top candidates will be able to write fused kernels for common hot paths, understand when to make use of lower level features like tensor cores or warp intrinsics, and will understand where these tools can be most impactful.
• Experience writing high-performance parallel C++. Bonus if done within an ML context with PyTorch, like for data loading, data processing, inference code.
• Experience building inference / demo prototype code (incl. Gradio, Docker etc.).