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Positron

Memory-first AI inference silicon for LLMs

Senior Software Engineer

Systems Software EngineeringUpdated on Nov 12
Full-timeRemote in USA

About the Job

Requirements

  • Seven or more years of professional experience in C++ software development, focused on performance-critical applications.
  • Strong understanding of C++ templates and modern memory management.
  • Hands-on experience with SIMD programming, including AVX-512, SSE, or equivalent, and intrinsics-based vectorization.
  • Experience in high-performance computing, numerical computing, or machine-learning inference optimization.
  • Experience optimizing machine-learning model execution, including tensor computations and memory access patterns.
  • Knowledge of multithreading, NUMA architectures, and low-level CPU optimization.
  • Proficiency in systems-level software development, profiling tools such as Perfetto, VTune, and Valgrind, and benchmarking.
  • Experience with hardware accelerators such as FPGAs, GPUs, or custom ASICs, and designing efficient software-hardware interfaces.

Responsibilities

  • Design and implement high-performance inference software for large language models on custom hardware.
  • Develop and optimize C++ libraries that efficiently use SIMD instructions, threading, and memory hierarchy.
  • Work with FPGA and systems engineers to ensure efficient data movement and computational offloading between x86 CPUs and FPGAs.
  • Optimize model execution using low-level optimizations, including vectorization, cache efficiency, and hardware-aware scheduling.
  • Contribute to performance profiling tools and methodologies to analyze execution bottlenecks at instruction and data-flow levels.
  • Apply NUMA-aware memory management techniques to optimize memory access patterns for large-scale inference workloads.
  • Implement machine-learning system-level optimizations such as token streaming, key-value cache optimizations, and efficient batching for transformer execution.
  • Collaborate with machine-learning researchers and software engineers to integrate model quantization techniques, sparsity optimizations, and mixed-precision execution.
  • Ensure code contributions include unit, performance, acceptance, and regression tests within a continuous-integration-based development process.

Desired Qualifications

  • Familiarity with LLVM/Clang or GCC compiler optimizations.
  • Experience in large language model quantization, sparsity optimizations, and mixed-precision computation.
  • Knowledge of distributed inference techniques and networking optimizations.
  • Understanding of graph partitioning and execution scheduling for large-scale machine-learning models.

About the Company

Positron

Memory-first AI inference silicon for LLMs

Positron develops memory-first AI inference silicon and systems to scale large-language-model serving. Its Atlas systems rely on commodity LPDDR5X memory rather than expensive HBM/CoWoS to boost bandwidth and capacity, enabling more efficient LLM deployment at hyperscale. The company’s next-generation Asimov silicon (tape-out planned for late 2026; production in 2027) pairs compute with very large memory per chip (roughly 288 GB to 2,304 GB) and is designed to be combined into Titan configurations (four to eight Asimov chips) for extremely large models and long context windows. Positron has already deployed Atlas systems in the cloud, including 50+ racks at Oracle Cloud Infrastructure and partnerships with Parasail, Jump Trading, and i3d.net. Its goal is to provide scalable, memory-rich AI inference hardware that lowers cost and increases throughput for serving large language models.

Company size
51-200
Company stage
Series C
Total funding
$1.2B
Headquarters
Reno, NV
Founded
2023

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