ML Optimisation Engineer (AI)

Location: London, United Kingdom; hybrid working model with in-person collaboration in office spaces and remote work.

Company

hirify.global is building an AI platform for autonomous driving that enables vehicles to learn from real-world experience and adapt across different environments and OEM platforms.

What you will do

• Own end-to-end delivery of model releases from requirements and training through evaluation and deployment readiness.
• Train and iterate on deep learning models in PyTorch using hypothesis-driven experiments and ablations.
• Debug model performance, identify regressions, determine root causes, and propose fixes.
• Apply optimisation techniques such as quantisation, distillation, and low-rank methods to meet on-vehicle runtime constraints.
• Collaborate with ML and performance engineering teams to define bottlenecks, align optimisation priorities, and hand off models.
• Communicate delivery timelines, trade-offs, and readiness criteria to stakeholders.

Requirements

• Proven experience improving production-system performance under latency, memory, bandwidth, power, thermal, or cost constraints.
• Strong hands-on experience training and iterating on deep learning models in PyTorch.
• Proficiency with at least one relevant toolchain, such as TensorRT, CUDA, Qualcomm QNN, Triton, or OpenCL.
• Ability to work across high-level model behaviour and low-level kernel or runtime execution.
• Knowledge of model optimisation concepts, including quantisation and/or distillation.
• Strong engineering fundamentals and collaboration skills.

Nice to have

• Experience with edge, embedded, or real-time models operating under tight latency and efficiency constraints.
• Experience across the ML lifecycle from training through deployment handoff.
• Experience benchmarking embedded or edge deployments on real devices.

Culture & Benefits

• Hybrid working with core hours and hands-on access to vehicle workshops and labs.
• Relocation support and visa sponsorship where applicable.
• Market-benchmarked salaries, equity, and location-dependent benefits.
• Learning and development budgets for training, conferences, and professional growth.
• Health, dental, retirement or pension, parental leave, therapy access, wellbeing partnerships, and team socials.

Hiring process

• Initial recruiter call followed by a hiring manager meeting.
• Deep-dive technical interviews covering programming, systems, and domain-specific topics.
• Final interview focused on mission and values alignment.