AI/ML Engineer
Vertice
| Company | Vertice |
| Category | Engineering |
| Location | Brno |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 30 Jan 2025 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
Spend on technology is one of the biggest items in an enterprise CFO’s budget, second only to headcount. Traditional procurement is one of the most frustrating, friction-filled bottlenecks in modern businesses. A single purchase routinely stalls for months, drowning in endless email threads and dragging in teams from Finance, Legal, Security, and IT.
Vertice is the world’s first Intelligent Procurement Platform aiming to eliminate this chaos. By combining automated agentic workflows, deep AI insights, and a global network of expert negotiators, we empower enterprises to buy smarter and scale at pace.
The market response has been unprecedented:
- $75bn+ in spend processed across our platform.
- 20% savings consistently delivered to our customers.
- 70% reduction in standard enterprise procurement cycles.
- 13X revenue growth achieved over the last two years alone.
Founded by serial technology entrepreneurs Roy and Eldar Tuvey, who have successfully scaled and exited two category-defining platforms and created $600M+ in enterprise value, we are backed by over $100M in Series C funding from tier 1 global investors including Bessemer Venture Partners, 83North, and Lakestar. We were recently recognized as the #2 fastest-growing company on the Sunday Times' Tech 100 and named the fastest-growing startup in the UK and Ireland by the FT’s Sifted.
With headquarters in London and hubs in New York, Brno, Sydney, and Johannesburg, we are fundamentally reshaping the global B2B economy.
How we work at Vertice Engineering
Collaboration and transparency are at the core of how we work. We keep it simple - no unnecessary management layers, just direct connections between business and engineering. Hierarchies don’t get in the way, instead - we keep things flat, flexible and focused on what works. This is how we do it:
- Autonomous Teams - Our small teams (made up of Frontend, Backend, Fullstack and QA) operate without traditional tech leads or managers. They decide how they work - whether it’s stand-ups, prioritization or project management methods. Teams are assigned areas/goals/problems where they can make the biggest impact, yet they don’t own any part of the code, so they can basically work on any part of the product.
- People Managers - You won’t be left figuring things out alone. Our People Managers with psychology backgrounds focus on your growth, well-being and career support while keeping Vertice’s flat, collaborative culture strong. They’re here to encourage open communication, support conflict resolution and help ensure you thrive.
- Chapters - Specialists still need to stay aligned. Chapters bring together people with similar expertise - like Frontend, Test Automation or Release - to share best practices, standards, common sense and they keep the knowledge flowing across teams - directly!
YOUR ROLE AT VERTICE
At Vertice, we build AI systems that run in production and solve real business problems end-to-end.
As an AI/ML Engineer, you will design, build, and operate hands-on AI solutions that are deeply integrated into our product and workflows. You will spend most of your time engineering systems: working real-world data, building intelligent services, integrating modern language models, and shipping reliable features that users depend on every day. You will collaborate closely with product managers and engineers to turn ambiguous problems into working AI-powered capabilities - from idea to production.
WHAT YOU’LL WORK ON
- Designing and implementing end-to-end AI solutions
- Building intelligent, LLM-powered systems for text- and document-heavy use cases
- Working with unstructured data (e.g. documents, PDFs, text streams) and turning it into usable signals
- Integrating and operating AI components as part of larger backend systems
- Continuously improving robustness, quality, and performance of AI-driven features
- Owning what you build: monitoring, iterating, and improving
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