Senior Machine Learning Engineer (Autonomous Vehicles)

Location: London, United Kingdom. Hybrid working model with in-person collaboration in office spaces, remote work, and hands-on work in vehicle workshops and labs.

Company

hirify.global builds an end-to-end AI platform for autonomous driving that learns directly from real-world experience and supports scalable deployment across vehicle manufacturers.

What you will do

• Lead large-scale data enrichment and curation initiatives for the Core Model Safety team.
• Build workflows for data enrichment, hard and rare event mining, and model introspection using fleet and heterogeneous real-world data.
• Design and train out-of-distribution detection mechanisms for robust driverless operation.
• Collaborate across pre-training, mid-training, reward modelling, self-supervised labelling, research, simulation, evaluation, and applied engineering.
• Shape the architecture across model engineering, infrastructure, compute, and data teams.
• Improve the efficiency and reliability of the machine learning operations stack.

Requirements

• Hands-on experience with machine learning systems deployed in the real world.
• Proficiency in Python and PyTorch, with strong software engineering practices.
• Experience building reliable machine learning training and evaluation systems.
• Strong experience with ML operations, infrastructure, distributed computing environments, and large-scale inference.
• Senior-level ownership, technical leadership, and collaboration across research and engineering boundaries.
• Clear written and verbal communication skills.

Nice to have

• Experience in autonomous vehicles or robotics, including deployment and closed-loop validation on physical systems.
• Experience mining, generating, or evaluating rare events using simulation and fleet data.
• Experience with cloud monitoring, dashboards, logging, and real-time alerts.
• Experience with transformer-based, multimodal, vision-language, or vision-language-action models.
• Proficiency in C++, CUDA, distributed training, or production ML performance optimization.

Culture & Benefits

• Hybrid working with core hours and collaboration across international hubs.
• Relocation support and visa sponsorship where applicable.
• Market-benchmarked salaries and meaningful equity.
• Learning and development budgets for training, conferences, and professional growth.
• Health insurance, dental coverage, enhanced parental leave, retirement or pension benefits where applicable, therapy access, wellbeing partnerships, and team socials.
• An ownership-focused environment where processes and ways of working are still being developed.

Hiring process

• 30-minute initial recruiter call.
• Two-hour programming and system design interviews.
• Two-hour domain machine learning deep-dive interviews.
• One-hour mission and values alignment interview.