About Accelerant Accelerant is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers.
About the role
We're looking for someone to own how machine learning and AI run in production at Accelerant. You'll lead a small engineering function responsible for the platform our data scientists build on. That covers data and feature pipelines, training and inference services, deployment, monitoring, and the infrastructure behind our agentic AI work. You'll set the standards, coach the team, and be accountable for the whole thing staying up.
What they're looking for
- Substantial experience running machine learning systems in production, including everything that happens after launch
- Strong engineering foundations. Python, infrastructure as code, containers and orchestration, and depth in at least one major cloud provider with sound instincts about cost and failure modes
- Data engineering capability, pipelines, orchestration, storage and access patterns, and enough SQL to hold your own in a warehouse
- A track record of integrating systems across organisational boundaries, including the part where you have to influence teams you don't manage
- Enough statistical literacy to have a real conversation with a data scientist about whether a model is working, and to stay skeptical when the dashboards say it is
- Experience leading or coaching engineers, plus judgement about which infrastructure will pay for itself and which is merely satisfying to build
More about this role
We're looking for someone to own how machine learning and AI run in production at Accelerant. You'll lead a small engineering function responsible for the platform our data scientists build on. That covers data and feature pipelines, training and inference services, deployment, monitoring, and the infrastructure behind our agentic AI work. You'll set the standards, coach the team, and be accountable for the whole thing staying up.
Much of the value in this role sits at the seams. Our machine learning systems are not an island. They need to exchange data and decisions with the wider Accelerant platform, with third-party providers, and with systems owned by other engineering teams. Designing those integrations, and building the working relationships with the people on the other side of them is closer to the centre of this job than any single piece of infrastructure.
We take the operational side seriously. We care about reproducibility, by which we mean knowing which data and which code produced any model currently making decisions. We care about training and serving computing features the same way, because the times they don't are the ones that hurt. We think about what we call the...
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