AI/ML Infra Intern: Build Scalable ML Pipelines

General Motors LLC

Sunnyvale (CA)

Hybrid

USD 107,000 - 132,000

Full time

5 days ago
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Benefits offered by this job

Paid US GM Holidays
Vehicle Discount Program
Growth opportunities

Job summary

General Motors LLC is seeking a software-oriented intern to help build and optimize AV ML infrastructure in Sunnyvale, CA. You will improve data processing pipelines, scheduling, and model training/inference workflows while assisting with testing infrastructure like Carbench/HIL.

You will collaborate with engineers to develop scalable tools, instrument systems, and monitor performance, aiming to reduce latency and accelerate autonomous driving experiments.

Qualifications

  • Enrolled in full-time degree program in CS or related field.
  • Experience in systems software or algorithms.
  • Proficiency in OO languages (Java, C++, Python).
  • Experience coding in Java, C/C++, Perl, PHP, Go or Python.
  • Strong communication and cross-functional collaboration.
  • Available to work full-time, 40 hours per week.

Responsibilities

  • Develop scalable infrastructure to support model training and inference.
  • Collect and synthesize requirements; build feature roadmaps.
  • Code deliverables in tandem with engineering team.
  • Adapt ML methods for distributed environments (clusters, multicore, GPUs).
  • Perform responsibilities varying by team.

Skills

Java
C++
Python
Go
Perl
PHP

Education

Bachelor's or Master's in computer science or related field

Tools

Hadoop/HBase/Pig
MapReduce/Sawzall/Bigtable
Spark
Ray
Kubernetes
Slurm

Job description

General Motors LLC is seeking a software-oriented intern to help build and optimize AV ML infrastructure in Sunnyvale, CA. You will improve data processing pipelines, scheduling, and model training/inference workflows while assisting with testing infrastructure like Carbench/HIL.

You will collaborate with engineers to develop scalable tools, instrument systems, and monitor performance, aiming to reduce latency and accelerate autonomous driving experiments.

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