Remote Lead ML Engineer, Localization

Doist; United States; Remote; USD 235,000 - 285,000; Full time

Benefits offered by this job

Comprehensive healthcare

Job summary

May Mobility, a leader in autonomous mobility, seeks a Lead ML Engineer to own the localization stack and architect state estimation for safe, scalable vehicle navigation. The role emphasizes feature extraction from camera, LiDAR, and radar, and real-time performance within diverse operating domains.

You will lead research, design, and deployment of neural architectures for robust localization, while driving data strategies, automated labeling, and active learning to handle long-tail scenarios.

Qualifications

• Ph.D. or Master’s degree in CS/EE/Robotics or related field.
• 7+ years of industry experience developing and deploying ML/DL models for computer vision or localization at scale.
• Expertise in ML/DL development using PyTorch or TensorFlow, including data curation and active learning.

Responsibilities

• Architect and drive the technical roadmap for a production-grade localization ML stack spanning vision, LiDAR, radar sensors.
• Lead research, design, training, and validation of neural architectures for localization features.
• Own end-to-end data strategy including data curation, auto-labeling, synthetic data, and active learning pipelines.
• Develop metrics and evaluation frameworks for localization performance across diverse ODDS.
• Define and validate failure modes and safety criteria for localization features.

Skills

PhD/Master level degree in CS/EE/Robt

7+ years ML/DL for CV or localization

PyTorch or TensorFlow

Python

C++

Linux development

Real-time ML optimization

Leadership & roadmap ownership

SLAM / localization concepts

Vision-Language Models / Foundation ML

Education

Ph.D. or Master’s degree in Computer Science, Electrical Engineering, Robotics, or related field

Tools

PyTorch

TensorFlow

Job description

May Mobility, a leader in autonomous mobility, seeks a Lead ML Engineer to own the localization stack and architect state estimation for safe, scalable vehicle navigation. The role emphasizes feature extraction from camera, LiDAR, and radar, and real-time performance within diverse operating domains.

You will lead research, design, and deployment of neural architectures for robust localization, while driving data strategies, automated labeling, and active learning to handle long-tail scenarios.