Senior Software Engineer, Machine Learning Inference

Founded in 2017, Syntiant is a leader in delivering hardware and software solutions for edge AI deployment, producing ultra-low-power deep neural network processors.
$180,000 - $230,000
Machine Learning
Senior Software Engineer
In-Person
5+ years of experience
AI · Enterprise SaaS

Description For Senior Software Engineer, Machine Learning Inference

Syntiant Corp., a pioneering company in the AI software and semiconductor solutions space, is seeking a Senior Software Engineer of Machine Learning Inference to join their team. This role presents an exciting opportunity to work at the intersection of machine learning and hardware optimization.

The position involves developing core building blocks that power computer vision, speech, and other application-specific APIs, with a strong emphasis on computational and numerical efficiency. The company's approach has achieved significant improvements over industry standards through fundamental ML innovations within a generalized production framework.

As a Senior Software Engineer, you'll be working with cutting-edge technology, optimizing performance for various hardware platforms including CPUs, DSPs, NPUs, and GPUs. The role requires a deep understanding of both machine learning concepts and low-level system optimization, making it perfect for someone who enjoys working at the intersection of ML and systems engineering.

The company, founded in 2017 and headquartered in Irvine, California, is backed by prominent investors including Intel Capital, Microsoft's M12, and the Amazon Alexa Fund. Their technology is being deployed globally across various consumer and industrial applications, from earbuds to automobiles.

This role offers competitive compensation, with a base salary range of $180,000 to $230,000, and the opportunity to work on challenging problems in edge AI deployment. The position requires 5+ years of industry experience and strong expertise in C/C++ programming, making it ideal for experienced engineers looking to make a significant impact in the AI hardware space.

The work environment is collaborative, involving interaction with hardware architects and other engineering teams. You'll be contributing to a technology that's pushing the boundaries of what's possible in edge AI, making this an excellent opportunity for professional growth and innovation in a rapidly evolving field.

Last updated 15 days ago

Responsibilities For Senior Software Engineer, Machine Learning Inference

  • Develop, optimize, and maintain high-performance C/C++ software for CPUs, DSPs, NPUs, and GPUs
  • Profile and optimize inference code for power, latency, and throughput on edge devices
  • Develop tools to benchmark and profile AI models on resource-constrained platforms
  • Research and implement state-of-the-art ML models with focus on computational efficiency
  • Debug and analyze performance bottlenecks in large-scale deployable software
  • Collaborate with hardware architects to co-optimize software and hardware solutions

Requirements For Senior Software Engineer, Machine Learning Inference

  • BS in Computer Science/Engineering or related field (MS or PhD preferred)
  • 5+ years of industry experience in software development and optimization
  • Strong C/C++ programming skills with a focus on performance optimization
  • Experience programming numerical algorithms in ML or related fields
  • Strong understanding of linear algebra and numerical optimization
  • Hands-on experience optimizing code for edge devices
  • Deep understanding of low-level computing, memory hierarchies, and parallel processing techniques
  • Experience with assembly-level optimizations, vectorization (SIMD), and low-level debugging
  • Familiarity with profiling tools such as perf, gprof, Valgrind, Intel VTune, NVIDIA Nsight
  • Experience with machine learning inference frameworks
  • Knowledge of AI model compression, quantization, and pruning techniques
  • Knowledge of compiler optimizations and JIT compilation techniques
  • Experience working in large, complex codebases with version control tools like Git

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