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May Mobility

Lead Machine Learning Engineer - Localization

RemoteUnited States only
Published
Role
AI / ML
Experience
Lead
$235k–$285k/yr
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Open to US only. Set where you work from to check your eligibility.

No BS summary

Lead ML engineer for autonomous vehicle localization, with 7+ years deploying ML/DL models for computer vision or localization at scale. Must have strong Python and/or C++, PyTorch or TensorFlow, real-time ML optimization, and experience with imagery, LiDAR, and/or radar. US remote role with moderate travel.

Core skills

PyTorch/TensorFlowPython/C++Computer Vision

Required skills

Linux

Optional skills

Vision-Language ModelsFoundation ModelsSLAMSensor fusion

What you'll do

  • Architect and drive the technical roadmap for a production-grade localization machine learning stack, spanning map and sparse landmark-based localization (vision/LiDAR/radar), optimized for real-time performance, robustness against sensor degradation, and integration with the broader autonomy system across diverse Operational Design Domains (ODDs).
  • Lead the research, design, training, and validation of advanced neural architectures, including object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction to extract and model localization features for robust localization.
  • Drive major feature development from inception to deployment, including high-level architecture design, rigorous code reviews, automated testing, mentorship of junior engineers, and technical resolution.
  • Own the end-to-end data strategy for the localization feature extraction domain, defining data curation, auto-labeling, synthetic data, and active learning pipelines to capture and resolve long-tail scenarios.
  • Develop robust metrics and evaluation frameworks for localization performance, including feature extraction accuracy, temporal consistency, and system-level reliability across diverse ODDs.
  • Define and validate failure mode and degradation criteria for localization features across ODDs, ensuring safety case coverage and graceful fallback behavior under sensor or model failure.
  • Evaluate, adapt, and integrate frontier techniques, including multimodal localization and vision/fusion foundation models, translating research advances into production-ready solutions.
  • Drive cross-functional alignment, translating complex autonomy goals into clear software and system requirements.

What they require

  • Ph.D. or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related field with a strong mathematical and engineering foundation.
  • 7+ years of industry experience developing and deploying ML/DL models for computer vision or localization at scale.
  • Deep expertise in several of the following areas: Computer Vision Foundations: Object detection, classification, segmentation, tracking, depth estimation, 3D reconstruction, and feature detection/description (e.g., SIFT, ORB, SuperPoint).
  • Deep expertise in vectorized landmark and feature detection networks, BEV-based scene representation, and temporal modeling.
  • Deep expertise in self-supervised/semi-supervised learning, open-vocabulary detection, and vision/fusion Foundation Models.
  • Experience with feature extraction and/or fusion from imagery, LiDAR, and/or radar.
  • Expertise in ML/DL development using PyTorch or TensorFlow, including experience with synthetic data generation, large-scale dataset handling, data curation, and active learning strategies.
  • Strong programming skills in Python and/or C++ with experience in modular software design and Linux-based development.
  • Expertise in ML optimization for real-time products with limited compute, such as quantization and pruning of large transformer models.
  • Proven leadership in developing technical roadmaps, mentoring engineers, and driving measurable improvements in model performance and system reliability.
  • Preferred: 10+ years of experience in ML/DL for autonomous driving or ADAS systems.
  • Preferred: Experience utilizing Vision-Language Models (VLMs) and/or Foundation Models for auto-labeling and long-tail (edge-case) detection.
  • Preferred: Working knowledge of localization and state estimation concepts (e.g., SLAM, sensor fusion).
  • Preferred: A proven record of inventions and/or publication record at top-tier conferences (e.g., CVPR, NeurIPS, ICCV, ECCV, ICLR).
  • Standard office working conditions which includes but is not limited to: prolonged sitting, prolonged standing, prolonged computer use.
  • Travel required? - Moderate: 11%-25%

Benefits

  • Comprehensive healthcare suite including medical, dental, vision, life, and disability plans.
  • Domestic partners who have been residing together at least one year are also eligible to participate.
  • Health Savings and Flexible Spending Healthcare and Dependent Care Accounts available.
  • Rich retirement benefits, including an immediately vested employer safe harbor match.
  • Generous paid parental leave as well as a phased return to work.
  • Flexible vacation policy in addition to paid company holidays.
  • Total Wellness Program providing numerous resources for overall wellbeing.

Automotive Technology

🇺🇸 United StatesAutomotiveStartupmaymobility.com

Details

Apply routeGreenhouse
$235k–$285k/yr