Machine Learning Engineer (Autonomous Driving)

TL;DR

Machine Learning Engineer (Autonomous Driving): Developing and improving end-to-end driving models for autonomous vehicles with an accent on sequential models, control, and planning. Focus on designing ML-driven behaviors, building evaluation pipelines, and integrating production-scale learning systems for real-world deployment.

Location: Must be based in or able to work from the office in Leonberg, Germany (Hybrid policy).

Company

hirify.global is a leading developer of Embodied AI technology, creating mapless and hardware-agnostic AI products to accelerate the transition to automated driving.

What you will do

• Develop and improve end-to-end driving models with state-of-the-art performance and robustness.
• Lead projects on personalized and collaborative driving, including behavior conditioning and comfort tuning.
• Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance.
• Curate and mine real-world and synthetic data to drive scenario diversity and feature development.
• Influence architecture choices, training methodologies, and deployment pathways for production systems.
• Collaborate cross-functionally to ensure integration and iteration velocity.

Requirements

• Must be based in or able to work from the office in Leonberg, Germany.
• Extensive track record of shipping deep learning systems to production.
• Expertise in deep learning, specifically sequential models, control, planning, or perception.
• Proficiency in Python, C++, and CUDA with a solid foundation in software engineering.
• Experience with ML frameworks, particularly PyTorch.
• Experience with real-time systems or robotics, ideally with simulation or vehicle-in-the-loop components.

Nice to have

• Prior work in autonomous driving, imitation learning, or trajectory prediction.
• Familiarity with personalization, human behavior modeling, or driver intent inference.
• Experience integrating ML systems into production hardware or multi-agent simulation.

Culture & Benefits

• Hybrid working policy combining office collaboration and remote flexibility.
• Core working hours to support schedule flexibility.
• Inclusive and diverse work environment focused on groundbreaking AI solutions.
• Opportunities to shape the long-term technical direction of autonomy.