Full-Time

Senior – Generative AI Deployment Engineer

Recogni

Recogni

51-200 employees

High-efficiency AI system for autonomous vehicles

Automotive & Transportation
AI & Machine Learning

Senior

San Jose, CA, USA

Required Skills
Python
Requirements
  • 3+ years of relevant software engineering experience
  • Proficiency in C++, Python, and modern machine learning frameworks
  • Experience in optimizing and deploying machine learning models, preferably LLMs for efficient inference
  • Good understanding of and ideally experience with high-performance distributed computing
  • Familiarity with hardware accelerators (GPUs, TPUs)
  • Experience with open-source compiler technologies such as TVM, XLA, MLIR, or LLVM
  • Equal opportunity employer
Responsibilities
  • Contribute to the design and implementation of the software stack for AI inference hardware
  • Participate in the co-design of hardware and software stack, particularly in the area of parallelizing AI models
  • Collaborate closely with machine learning engineers to identify the requirements of future machine learning workloads
  • Benchmark, analyze, and optimize the performance of key software components of the AI deployment software stack

Recogni stands out in the automobile industry with its unique approach to designing a vision-oriented inference artificial intelligence system, delivering an unprecedented 500x better power efficiency compared to other solutions. This enables edge processing at multiple points on vehicles, reducing the need for central processing and accelerating the development of fully-autonomous vehicles. The company's strong foundation in high-performance computing, artificial intelligence, machine learning, and imaging and vision systems, coupled with its commitment to user privacy and data security, make it a promising place to work and grow.

Company Stage

Series C

Total Funding

$233.1M

Headquarters

San Jose, California

Founded

2017

Growth & Insights
Headcount

6 month growth

23%

1 year growth

27%

2 year growth

74%
INACTIVE