Syndio reviews

I cannot earnestly give five stars as I was laid off from the company six months after my position was created and I was hired. I was reassured this was not performance related, and I'd have been kept on if at all possible. This is sadly indicative of poor long-term planning and a disregard for the workforce's wellbeing. I made clear in interviews I was looking for a stable opportunity I could stay in for a good few years, where I could grow as an engineer. To be hired on this premise then let go so soon is very disappointing. Recently (at time of writing) there been has somewhat worrying amount of turnover and shuffling in the more senior echelons of the organisation. Leadership is yet to address this, and concerns are rising.

There is a growing gap between the external story sold and the internal execution reality. Leadership talks confidently about AI, product transformation, and strategic partnerships, but day to day, the engineering organization often feels under-resourced, reactive, and misaligned with the ambition being communicated. Product direction is shifting quickly toward AI-enabled compensation workflows, but the foundational data infrastructure does not appear to be keeping pace. In my experience, data structure, consistency, and completeness remain recurring challenges, making it difficult to build reliable and auditable capabilities at the pace leadership expects. AI adoption internally feels more performative than mature. Despite the company’s AI-first narrative, the internal tooling has not produced a meaningful productivity improvement in my experience, and adoption appears much lower than leadership messaging suggests. There is enthusiasm for AI-assisted development, but less evidence that these workflows are improving delivery, reliability, or engineering quality in practice. Work often feels siloed across product, engineering, and leadership. Teams are not consistently aligned on design decisions, implementation plans, or launch readiness, which leads to frequent miscommunication, unclear ownership, and avoidable rework. Product timelines often seem to move faster than production readiness. In my experience, teams are sometimes expected to support launches before the architecture, testing, monitoring, documentation, and operational ownership are mature enough. The company is trying to move quickly into AI-native products, but parts of the legacy software stack, CI/CD process, and developer experience feel dated. This creates avoidable engineering friction and makes it harder for teams to deliver production-ready software at the pace leadership expects. The company is also losing or reducing too much senior technical context. That creates a difficult environment for the people who remain, especially when newer or less experienced engineers are expected to execute on complex enterprise commitments without enough experienced technical leadership around them. Compensation and talent strategy also appear to be contributing to retention issues. The company seems increasingly dependent on geographic cost arbitrage rather than building a compelling senior engineering culture. That may reduce costs in the short term, but it makes it harder to hire and keep the exact people needed for a complex technical pivot.