Senior Backend Engineer: Machine Learning Infrastructure

The ML Infrastructure team builds and operates the shared backend services and platform capabilities that Constructor's ML and product teams rely on to develop, deploy, and run machine learning at scale - from model serving (including LLMs) to the specialized storages and data pipelines behind it.

We are a service team by nature: our roadmap is shaped by the needs of product teams, and we succeed when they can move faster with less friction. We're looking for a Senior Backend Engineer who is comfortable with, and motivated by this dynamic, and who will take end-to-end ownership of core services.

In the near and mid-term, our focus areas include:

- Making ML workloads easy to run, scale, and operate for every team

- Providing self-service, configurable infrastructure components to ML and product teams

- Owning and evolving core ML services and related pipelines with a high bar for reliability and performance

CHALLENGES YOU WILL TACKLE

- Design, build, and operate high-load distributed backend services that power the company's ML infrastructure

- Take end-to-end ownership of core ML services and related data pipelines: from design and implementation to deployment, observability, and continuous improvement

- Partner with ML and product teams to understand their needs and turn them into reliable, reusable platform capabilities

- Make and defend technical decisions: evaluate trade-offs, design first, and own the outcome

- Be responsible for what you and your team do

- 5+ years of professional experience in backend or platform engineering

- Extensive Python knowledge (our primary language)

- Hands-on experience developing on a public cloud (AWS, GCP, or Azure) or with self-managed Kubernetes - AWS is our primary platform, but we're not limited to it

- Experience designing and building distributed, high-load services and APIs

- Strong knowledge of data structures, algorithms, and their trade-offs

- Design-driven engineering mindset: we're a coding-assistant-friendly environment and expect you to use modern AI tools - but you lead with system design and a strong understanding of their applicability and trade-offs