ML Engineer II — Autonomous Driving Pipelines

Aimlroles; Northern (KY); Hybrid; USD 160,000 - 210,000; Full time

Benefits offered by this job

Comprehensive healthcare

Retirement plan with employer match

Paid parental leave

Flexible vacation

Total Wellness Program

Job summary

May Mobility is expanding its first-of-its-kind autonomous shuttle and mobility services across the nation. We are seeking ML-Oriented Software Engineers with robotics or AV background to join our Autonomous Driving ML team and design pipelines that improve our stack at scale.

You will own data, training and evaluation pipelines across cloud and cluster environments, and help build training data stores and metadata to support rapid experimentation.

Qualifications

• Bachelor’s or Master’s degree in Robotics, Computer Science or a related field with strong mathematical and engineering foundations.
• A minimum of 2 years building ML-oriented infrastructure, platforms, or distributed systems in production.
• Proficiency in C++, Python and PyTorch with experience in Linux environments.
• Familiarity with basic concepts in Machine Learning (training loops, basic operators and architectures)

Responsibilities

• Own the process to transition Autonomous Driving ML models from concept to commercial scale.
• Architect and operate data, training and evaluation pipelines across cloud and cluster environments.
• Design and maintain the data and metadata stores that back our training and evaluation workflows.

Skills

C++

Python

PyTorch

Linux

ML concepts

Data pipelines

Education

Bachelor’s or Master’s in Robotics or CS

Tools

Ray

Kubeflow

Airflow

MLflow

Weights & Biases

DeepSpeed

Spark

Parquet

Go

Rust

Job description

May Mobility is expanding its first-of-its-kind autonomous shuttle and mobility services across the nation. We are seeking ML-Oriented Software Engineers with robotics or AV background to join our Autonomous Driving ML team and design pipelines that improve our stack at scale.

You will own data, training and evaluation pipelines across cloud and cluster environments, and help build training data stores and metadata to support rapid experimentation.