Staff Tech Lead Manager, Machine Learning, Vision Models

Location: London, United Kingdom. This is a hybrid role based in the London office, combining office and workshop time with working from home.

Company

hirify.global develops embodied AI software and foundation models that enable vehicles to perceive, understand, and navigate complex environments for automated driving.

What you will do

• Lead and grow a team of four Senior Machine Learning Engineers in London, while hiring across MLE, software engineering, and data science profiles.
• Set the technical direction for adapting on-vehicle models and foundation models into scalable offline scene-understanding models.
• Own sprint, quarterly, and annual roadmaps, translating ambiguous goals into concrete technical programmes.
• Build production-grade ML systems for validation, customer deliverables, coverage analysis, rare-event mining, and autonomous-driving evaluation.
• Accelerate feedback between driving-model iterations and reliable model-development decisions.
• Align technical roadmaps with on-vehicle modelling, foundation model, evaluation, simulation, and platform teams across the UK and US.

Requirements

• 8+ years of ML engineering experience, including hands-on computer vision using camera and/or lidar sensor data.
• Experience shipping production ML systems from research through reliable, monitored, customer-facing software.
• 2+ years of line management or technical leadership for senior engineers, including hiring, development, and retention.
• Staff-level knowledge of transformer-based and multimodal architectures, foundation models, and large-scale training.
• Proficiency in Python and ML frameworks, especially PyTorch, with strong production engineering judgement.
• Strong cross-functional leadership and experience aligning roadmaps across teams and geographies.

Nice to have

• Knowledge of perception systems and 3D scene understanding for autonomous driving, including cuboid detection, lane estimation, depth estimation, and semantic enrichment.
• Experience with offline models, auto-labelling, model distillation, temporal or world models, or simulation and counterfactual evaluation.
• Experience leading or partnering with distributed teams across UK and US time zones.
• MS or PhD in Computer Science, Engineering, or a related field.

Culture & Benefits

• Full-time employment with a hybrid working policy.
• Time in offices and workshops supports collaboration, innovation, relationships, and learning.
• Inclusive interview experience with accommodations or adjustments available on request.
• Commitment to a diverse, fair, respectful, and inclusive workplace.