Hybrid-Remote AI/ML Engineer for Autonomous Driving

Aptiv; Poland; Hybrid; PLN 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work

Private health care

Life insurance

Well-Being Program

Pension Plan

Sports groups

Job summary

Aptiv is seeking a Master’s-level candidate to contribute to cutting-edge AI for ADAS and automated driving. You will apply machine learning and computer vision techniques to real-world automotive challenges and gain hands-on experience with large-scale datasets and production-oriented workflows.

You will collaborate with researchers and engineers in an international environment, exploring foundation models, multi-modal learning, and automated parking while developing both research and

Qualifications

• Master's degree (or close to completion) in Computer Science, ML, CV, Robotics, EE, Math, Physics, or related field.
• Strong analytical and problem-solving skills with formal math/ML background.
• Reading and understanding scientific literature and translating ideas into code.

Responsibilities

• Work on cutting-edge AI technologies powering ADAS and automated driving systems.
• Apply ML and CV techniques to real-world automotive challenges.
• Gain hands-on experience with large-scale datasets and production-oriented workflows.
• Collaborate with researchers and engineers in an international environment.
• Explore foundation models, multi-modal learning, and automated parking.

Skills

Python

Machine Learning

Computer Vision

Deep Learning

PyTorch

Research Skills

English Communication

Education

Master's degree in CS/ML/CV or related field

Tools

PyTorch

Git

Job description

Aptiv is seeking a Master’s-level candidate to contribute to cutting-edge AI for ADAS and automated driving. You will apply machine learning and computer vision techniques to real-world automotive challenges and gain hands-on experience with large-scale datasets and production-oriented workflows.

You will collaborate with researchers and engineers in an international environment, exploring foundation models, multi-modal learning, and automated parking while developing both research and