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Arago

Photonic AI processors for energy-efficient workloads

ML Systems Engineer - Inference Acceleration

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeÎle-de-France, FranceIn-person

About the Job

Requirements

  • Strong experience in high-performance machine-learning inference, graphics processing unit or accelerator programming, or machine-learning systems engineering.
  • Deep understanding of computer architecture, accelerator and graphics processing unit execution models, memory hierarchies, parallelism, and performance bottlenecks.
  • Experience developing and optimizing custom kernels using CUDA, Triton, ROCm/HIP, or equivalent low-level programming environments.
  • Experience with operator fusion, tiling, scheduling, data movement optimization, graph execution, and profiling of compute- and memory-bound workloads.
  • Strong understanding of distributed model execution, including tensor, pipeline, sequence, and/or expert parallelism and communication/computation overlap.
  • Hands-on experience with modern inference-serving systems such as vLLM, SGLang, TensorRT-LLM, or equivalent, including KV-cache management, continuous batching, paged attention, and prefill/decode scheduling.
  • Strong C++ and Python skills, and comfort working on a custom accelerator stack where the compiler, runtime, kernels, and abstractions are actively being developed.
  • English proficiency at a proficient level.

Responsibilities

  • Analyze modern artificial intelligence workloads and identify kernel-, runtime-, memory-, and system-level bottlenecks on Arago's accelerator.
  • Develop and optimize custom kernels, fused operators, and execution strategies to maximize device utilization.
  • Design efficient mappings of models and operators across multiple Arago devices, including communication and synchronization strategies.
  • Develop inference-serving techniques such as continuous batching, paged key-value caches, prefix/context caching, chunked prefill, and prefill/decode interleaving or disaggregation.
  • Build profiling, benchmarking, and performance-analysis infrastructure spanning kernels, full models, and serving workloads.
  • Work closely with Arago's hardware, compiler, and runtime teams to co-design software abstractions and influence future hardware features based on real model workloads.

Desired Qualifications

  • Exposure to or experience with MLIR and MLIR dialects.

About the Company

Arago

Photonic AI processors for energy-efficient workloads

Drafting a concise, plain-language company summary for Arago based on provided details.

Company size
51-200
Company stage
Seed
Total funding
$24.3M
Headquarters
Paris, France
Founded
2024

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