ML Engineer (Autonomous Driving)

Location: Tokyo, Japan; hybrid working with in-person collaboration in office spaces, remote work, core hours, and hands-on work in vehicle workshops and labs.

Company

hirify.global develops an end-to-end AI platform for autonomous driving, enabling vehicles to learn from real-world experience and adapt across different environments and vehicle platforms.

What you will do

• Design and deliver ML-driven behaviours for assisted and autonomous driving.
• Develop and improve end-to-end driving models for performance, robustness, and generalisation.
• Lead projects involving personalised and collaborative driving, behaviour conditioning, comfort tuning, and user alignment.
• Build evaluation pipelines and metrics for closed-loop and open-loop driving performance and product readiness.
• Curate and mine real-world and synthetic data to improve scenario diversity, coverage, and feature development.
• Influence architecture, training, and deployment decisions while collaborating across AI Platform, Simulation, Robot SW, and Model Release teams; mentor engineers and shape technical direction.

Requirements

• Proven experience shipping deep learning systems to production.
• Expertise in deep learning, particularly sequential models, control, planning, or perception.
• Proficiency in Python and relevant languages such as C++ and CUDA, plus ML frameworks including PyTorch.
• Strong software engineering foundations and experience with real-time systems or robotics.
• Experience with simulation- or vehicle-in-the-loop components.
• Ability to lead cross-team technical initiatives, drive alignment, and mentor engineers.

Nice to have

• Experience in autonomous driving, imitation learning, or trajectory prediction.
• Familiarity with personalisation, human behaviour modelling, or driver intent inference.
• Experience integrating ML systems into production hardware or multi-agent simulation.

Culture & Benefits

• Hybrid working with flexible remote time, core hours, and collaboration in dedicated offices, workshops, and labs.
• Relocation support and visa sponsorship are available where applicable.
• Market-benchmarked salaries and equity participation.
• Learning and development budgets for training, conferences, and professional growth.
• Benefits may include health and dental insurance, enhanced parental leave, retirement or pension schemes, therapy access, wellbeing partnerships, and team socials.
• An evolving environment with significant ownership in shaping processes and technical direction.

Hiring process

• Initial recruiter call lasting approximately 30 minutes.
• Competency interviews covering Python programming and PyTorch debugging, followed by deep-dive system design interviews.
• Final interview covering values alignment and applied machine learning.