Machine Learning Engineer (Generative Simulation)

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

Company

hirify.global develops an end-to-end AI platform for autonomous driving that learns from real-world experience and supports scalable deployment across vehicles.

What you will do

• Set technical direction and design key components for generative simulation systems.
• Develop efficient generative world models using diffusion, transformer, or hybrid architectures.
• Build interactive models for reinforcement learning, planning, and safety evaluation loops.
• Optimize performance from latent compression through context pruning to achieve low-latency roll-outs.
• Define metrics for long-horizon coherence, physics fidelity, and planner integration; run ablations and scaling studies.
• Integrate models into closed-loop training and evaluation, mentor junior researchers, and contribute to technical roadmaps and publications.

Requirements

• 4+ years of machine learning research or engineering experience focused on generative video or world models.
• Deep knowledge of diffusion and latent-video models, including sampling efficiency or model-throughput optimization.
• Experience with high-dimensional temporal or spatiotemporal data, such as video or multi-sensor fusion.
• Strong Python and PyTorch engineering fundamentals and experience building research-grade production tools.
• Strong publication record or contributions to open-source machine learning tooling.
• Ability to collaborate in a fast-paced, innovative, interdisciplinary environment.

Nice to have

• Passion for autonomous driving and willingness to learn beyond every listed requirement.

Culture & Benefits

• Hybrid work combining office collaboration with focused remote work.
• Relocation support and visa sponsorship where applicable.
• Equity participation and market-benchmarked salaries.
• Learning and development budgets for training, conferences, and growth.
• Health, dental, parental leave, retirement or pension benefits where applicable, therapy access, wellbeing partnerships, and team socials.

Hiring process

• Initial recruiter call lasting 30 minutes.
• Competency interviews covering programming, PyTorch debugging, and a hiring manager interview, followed by deep-dive systems and domain interviews.
• Final 45-minute mission and values interview.