The opportunity
We're hiring experienced performance engineers to find and land performance wins across the Supabase stack — from the query optimiser to block device level, and across the network paths and cloud infrastructure that connect our distributed platform. This is a performance…
What you'll do
Find bottlenecks in live production systems and characterize them precisely: enough that the owning team can act on them without you in the room.
Partner closely with database teams (e.g. Multigres, OrioleDB) and infra: teams to land concrete performance improvements.
Build, communicate, and evolve performance methodologies and tooling that: turn live production data into actionable insight.
Partner with the observability team to capture the right signals and: establish a unified view of platform performance (latency, throughput, tail behavior) across products.
Help teams self-serve performance analysis and make performance a first-class engineering concern.
What they're looking for
- Deep experience in performance engineering, specifically performance analysis of complex distributed systems.
- You can walk the whole stack: follow a tail-latency problem from the query optimizer through the syscall boundary to the IO scheduler — or across pooler hops, network paths, and availability zones — reading profiles and traces at every layer.
- Depth on at least one end of that stack: Linux kernel internals (virtual memory, scheduling, networking, IO) or database internals (query planning, storage engines, buffer and WAL management)
- Strong working knowledge of networking and cloud infrastructure performance:: TCP behavior under load, network virtualization overhead, and the performance characteristics of AWS primitives (EBS throughput and IOPS, instance network limits, placement groups, cross-AZ latency).
- Proven ability to turn performance insights into real product and: architecture improvements — including getting teams you don't belong to to prioritize and ship the fix.
- Strong written communication: your problem characterisations are precise, reproducible, and compelling on their own.
- Pragmatic, tooling-oriented mindset; comfort working across product, infra, and database teams.
