Engineering Manager - Compute and Data Platform (AI Platform)

Location: London, United Kingdom; able to work from the London office. Hybrid working combines office collaboration with remote work.

Company

hirify.global is building an AI platform for autonomous driving, using embodied, mapless, and hardware-agnostic AI to enable vehicles to learn from real-world experience.

What you will do

• Lead, coach, develop, and performance-manage the Compute Foundations engineering team.
• Hire and onboard engineers while developing technical leadership and ownership within the team.
• Own roadmap planning, project prioritisation, delivery commitments, KPIs, dashboards, risks, and dependencies.
• Guide a multi-cloud, multi-region platform for large-scale model training and inference across heterogeneous GPU architectures, including Nvidia Hopper, Blackwell, and Qualcomm.
• Oversee Kubernetes infrastructure and workload queuing and scheduling across GPU environments.
• Plan compute and storage capacity, balance reliability, performance, availability, and cost, and guide distributed storage, caching, and data-locality strategies.

Requirements

• Strong experience managing and developing an infrastructure, platform, or distributed systems engineering team, including hiring, coaching, performance management, and career development.
• Experience delivering through others, owning team priorities, and balancing delivery with operational reliability and longer-term platform improvements.
• Substantial experience with Kubernetes and workload queuing and scheduling systems such as Kueue or equivalent technologies.
• Ability to reason about availability, capacity, performance, and cost across complex cloud infrastructure, including compute, storage, and data movement.
• Understanding of large-scale GPU compute infrastructure for machine-learning training or inference and the operational challenges of scaling beyond small GPU deployments.
• Sound technical judgement and prior individual-contributor delivery experience, without needing to be the team’s primary individual contributor.
• Clear communication with technical teams, stakeholders, and external providers, and ability to turn ambiguous requirements into practical priorities.
• Ability to work from the London office and lead a team based there.

Nice to have

• Experience operating multi-cloud or multi-region platforms for large-scale AI workloads.
• Experience introducing new GPU architectures or managing heterogeneous GPU fleets.
• Experience with distributed storage, caching, and data-locality strategies for data-intensive workloads.
• Experience with capacity forecasting, cloud cost modelling, and close collaboration with cloud infrastructure providers.

Culture & Benefits

• Hybrid working with office collaboration, remote work, core hours, and opportunities to work hands-on in vehicle workshops and labs.
• Relocation support and visa sponsorship where applicable.
• Market-benchmarked salaries, meaningful equity, and comprehensive location-dependent benefits.
• Health, dental, retirement or pension, enhanced parental leave, therapy access, wellbeing partnerships, and team socials.
• Learning and development budgets supporting training, conferences, and professional growth.
• An evolving environment with significant ownership and no fully established playbook.

Hiring process

• Initial 30-minute recruiter call.
• Competency interviews covering system design and hiring-manager assessment, followed by deep-dive technical interviews covering technical leadership, code review, and people management.
• Final interview covering leadership, mission, and values alignment.