Senior Machine Learning Engineer - AV Core (Autonomous Vehicles)

Location: London, United Kingdom. Full-time hybrid role combining work from the London office and home.

Company

hirify.global develops assisted and automated driving technology, including foundational capabilities for collision avoidance, model understanding, and robustness under failure.

What you will do

• Lead large-scale data enrichment, curation, and rare-event mining for the Core Model Safety team.
• Build workflows supporting model introspection, robust performance, and evaluation across heterogeneous fleet and training data.
• Design and train out-of-distribution detection mechanisms for robust driverless operation.
• Collaborate with pretraining, midtraining, reward modelling, self-supervised labelling, and data enrichment teams.
• Shape interfaces and architecture across model engineering, infrastructure, compute, and data teams.
• Improve the efficiency and reliability of the machine learning and operations stack.

Requirements

• Hands-on experience with machine learning systems deployed in real-world environments.
• Strong proficiency in Python and PyTorch, with reliable software engineering and experience building ML training and evaluation systems.
• Experience with MLOps, infrastructure, distributed computing environments, and large-scale inference.
• Senior-level ownership of a substantial technical area and the ability to collaborate across research and engineering boundaries.
• Clear written and verbal communication skills.

Nice to have

• Experience in autonomous vehicles or robotics, including physical-system deployment and closed-loop validation.
• Experience mining, generating, or evaluating rare events using simulation and fleet data.
• Experience with cloud monitoring, dashboards, logging, and real-time alerts for large-scale operations.
• 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 policy combining time in offices and workshops with time working from home.
• Close collaboration with research, simulation, evaluation, and applied engineering.
• Access to large-scale training and fleet data.
• Focused senior team with substantial ownership and high-impact technical work.