Summary
✨ AI‑Generated
A senior backend engineering role building scalable Python services in a high-performance environment. The position offers ownership of architecture, deployment, and production systems.
Highlights
Design core backend systems from the ground up with significant technical ownership and impact.
Description
Senior Backend Engineer, Python and Kubernetes
San Francisco or New York | On-site
The opportunity
A recently launched multi-strategy hedge fund, one of the largest fund debuts in two decades, is building its core engineering platform from a clean sheet.
There is no legacy code, no tech debt, and no inherited migration.
The engineers who join now will choose the architecture the firm runs on for the next decade, with a multi-billion dollar capital base behind them and a deliberately small team where every engineer's work reaches the business.
The role
This is a hands-on backend seat.
You will design, build, and run Python services that researchers and investment teams use every day.
Kubernetes is the main runtime for everything you ship.
You will own each service end to end, from design through CI/CD, deployment, and running it in production.
What you will do
Build and ship Python microservices that support research and investment workflowsDeploy, operate, and scale those services on Kubernetes in productionOwn your CI/CD pipelines and the full path from commit to productionIntegrate with third-party systems and APIsPartner with researchers and product managers on service and schema design
What we are looking for
Around 3 to 10 years of backend engineering, with projects carried from design through productionExpert Python and strong SQLRecent, hands-on Kubernetes experience deploying and running production services.
This means working-engineer fluency with deployments, services, config, and scaling.
You do not need to have built clusters from scratch or contributed to Kubernetes itself.Microservices experience, including CI/CD you owned yourselfPostgreSQL or a similar relational databaseClear communication with engineers and non-engineers
Nice to have
AWS, REST, gRPC, or KafkaExposure to Spark, Ray, Polars, or similar data toolsGreenfield builds, or startups and trading firms where engineers own the whole stack