Machine Learning Engineer, Data Enrichment (Computer Vision)

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

Company

hirify.global builds an AI platform for autonomous driving that enables vehicles to learn from real-world experience and adapt across vehicle platforms and OEM deployments.

What you will do

• Define scene and scenario attributes, dataset requirements, acceptance criteria, annotation tasks, and labelling instructions with internal customers.
• Curate and deliver ground-truth datasets covering rare scenarios, class imbalance, and multiple countries and vehicle platforms.
• Build and evaluate computer vision models using hirify.global’s World Foundation Model to identify scene attributes and driving behaviours.
• Integrate classifiers with enrichment infrastructure for inference across existing and newly collected driving data, corpus analysis, and scenario mining.
• Use error analysis to investigate data, label, and model gaps and improve annotation, training, evaluation, and inference tools.
• Document dataset and classifier quality, limitations, and performance for colleagues across disciplines.

Requirements

• Practical experience developing and evaluating computer vision models for image or video understanding, including spatial relationships in visual data.
• Strong Python programming skills and experience with a modern machine learning framework such as PyTorch.
• Experience curating training and evaluation datasets and assessing annotation quality, including understanding class imbalance, data leakage, and representative evaluation.
• Experience taking machine learning models beyond experimentation into use through reproducible training, evaluation, and inference.
• Ability to investigate model failures and determine whether improvements require better data, clearer labels, or modelling changes.
• Ability to translate ambiguous customer requests into technical deliverables and quality criteria and communicate performance and limitations across disciplines.

Nice to have

• Experience with 3D geometry, multi-camera systems, or temporal scene understanding.
• Experience in autonomous driving, ADAS, or robotics.
• Experience adapting pretrained, foundation, or vision-language models.
• Experience with human annotation workflows, active learning, or model-assisted labelling.
• Experience with SQL, distributed data processing, and batch inference.

Culture & Benefits

• Hybrid working with core hours, office collaboration, and hands-on work in vehicle workshops and labs.
• Relocation support and visa sponsorship where applicable.
• Market-benchmarked salaries, meaningful equity, and location-dependent benefits.
• Learning and development budgets for training, conferences, and growth.
• Health insurance, dental coverage, enhanced maternity and paternity leave, retirement or pension provisions, therapy access, wellbeing partnerships, and team socials where applicable.
• An evolving work environment with significant ownership in shaping how the company operates.

Hiring process

• Initial recruiter call lasting approximately 30 minutes.
• Competency interviews covering Python programming and the machine learning engineering domain.
• Deep-dive technical interviews covering systems design and a hiring manager discussion, followed by a final mission and values interview.