About The Role

We are looking for an experienced Senior AI/ML Engineer to take ownership of the technical delivery of a core computer vision capability within SyncTech’s platform.

You will work with complex, real-world visual data captured across properties in highly variable conditions. Your focus will be developing AI systems that can reliably identify and understand property damage and transform complex visual information into structured, useful outputs.

This is not a research-only role. The models you build will operate within live insurance workflows and contribute to decisions involving real properties and real claims. Accuracy, robustness and the ability to demonstrate model performance matter.

Working closely with our AI Lead, you will translate SyncTech’s product vision, AI strategy and priorities into robust, production-ready ML capabilities. You will have significant ownership over technical execution — including experimentation, model development, evaluation and productionisation — while working within the direction and priorities set by the company and AI Lead.

We are looking for someone who combines deep technical capability with strong delivery discipline: someone who can investigate difficult problems, recommend solutions, challenge assumptions with evidence and take accountability for delivering agreed outcomes.

What You’ll Own

• Own the technical execution and delivery of defined computer vision and ML capabilities, aligned with the direction set by the company and AI Lead.
• Translate agreed product and AI priorities into well-structured technical problems, experiments and production solutions.
• Develop and fine-tune computer vision and vision-language models on complex, domain-specific visual data.
• Evaluate alternative technical approaches and make evidence-based recommendations to the AI Lead and wider team.
• Define and continuously improve the data, annotation and evaluation practices required to deliver reliable model performance.
• Build rigorous evaluation frameworks and perform systematic error analysis to objectively determine whether models meet production requirements.
• Drive work from investigation through experimentation, validation and production delivery, with clear milestones and measurable outcomes.
• Identify technical risks, dependencies and blockers early, communicate them clearly and develop pragmatic options to keep delivery moving.
• Work closely with the Tech, Product, Engineering Team and domain experts to ensure technical work remains aligned with broader company and customer outcomes.

About you

• A degree in computer science, engineering, data science, mathematics or a related field, or equivalent practical experience.
• Strong hands-on experience building and fine-tuning computer vision or vision-language models on domain-specific, real-world data.
• Deep understanding of modern computer vision, including detection, segmentation and complex multi-label or multi-instance problems.
• Practical experience with modern model fine-tuning techniques and frameworks, with the judgement to choose the right approach rather than simply the largest model.
• Strong foundations in model evaluation, experimentation and error analysis, with the ability to rigorously demonstrate whether an approach actually works.
• A data-centric mindset — you know when better data, labels or problem formulation will deliver more value than another model architecture.
• Strong production ML engineering skills, including Python, version control, reproducible experimentation, containers and GPU infrastructure.
• Experience with cloud-based ML infrastructure, ideally AWS, and building or working with production ML data pipelines.
• Good understanding of privacy, security and responsible handling of sensitive real-world data.
• Clear communication skills and the ability to translate complex technical findings into decisions others can act on.
• Strong delivery discipline. You can take an agreed technical objective, break it into a practical path forward, manage uncertainty and drive it through to a production-ready outcome.
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About The Role

We are looking for an experienced Senior AI/ML Engineer to take ownership of the technical delivery of a core computer vision capability within SyncTech’s platform.

You will work with complex, real-world visual data captured across properties in highly variable conditions. Your focus will be developing AI systems that can reliably identify and understand property damage and transform complex visual information into structured, useful outputs.

This is not a research-only role. The models you build will operate within live insurance workflows and contribute to decisions involving real properties and real claims. Accuracy, robustness and the ability to demonstrate model performance matter.

Working closely with our AI Lead, you will translate SyncTech’s product vision, AI strategy and priorities into robust, production-ready ML capabilities. You will have significant ownership over technical execution — including experimentation, model development, evaluation and productionisation — while working within the direction and priorities set by the company and AI Lead.

We are looking for someone who combines deep technical capability with strong delivery discipline: someone who can investigate difficult problems, recommend solutions, challenge assumptions with evidence and take accountability for delivering agreed outcomes.

What You’ll Own

• Own the technical execution and delivery of defined computer vision and ML capabilities, aligned with the direction set by the company and AI Lead.
• Translate agreed product and AI priorities into well-structured technical problems, experiments and production solutions.
• Develop and fine-tune computer vision and vision-language models on complex, domain-specific visual data.
• Evaluate alternative technical approaches and make evidence-based recommendations to the AI Lead and wider team.
• Define and continuously improve the data, annotation and evaluation practices required to deliver reliable model performance.
• Build rigorous evaluation frameworks and perform systematic error analysis to objectively determine whether models meet production requirements.
• Drive work from investigation through experimentation, validation and production delivery, with clear milestones and measurable outcomes.
• Identify technical risks, dependencies and blockers early, communicate them clearly and develop pragmatic options to keep delivery moving.
• Work closely with the Tech, Product, Engineering Team and domain experts to ensure technical work remains aligned with broader company and customer outcomes.

About you

• A degree in computer science, engineering, data science, mathematics or a related field, or equivalent practical experience.
• Strong hands-on experience building and fine-tuning computer vision or vision-language models on domain-specific, real-world data.
• Deep understanding of modern computer vision, including detection, segmentation and complex multi-label or multi-instance problems.
• Practical experience with modern model fine-tuning techniques and frameworks, with the judgement to choose the right approach rather than simply the largest model.
• Strong foundations in model evaluation, experimentation and error analysis, with the ability to rigorously demonstrate whether an approach actually works.
• A data-centric mindset — you know when better data, labels or problem formulation will deliver more value than another model architecture.
• Strong production ML engineering skills, including Python, version control, reproducible experimentation, containers and GPU infrastructure.
• Experience with cloud-based ML infrastructure, ideally AWS, and building or working with production ML data pipelines.
• Good understanding of privacy, security and responsible handling of sensitive real-world data.
• Clear communication skills and the ability to translate complex technical findings into decisions others can act on.
• Strong delivery discipline. You can take an agreed technical objective, break it into a practical path forward, manage uncertainty and drive it through to a production-ready outcome.
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