Distributed Systems Engineer
Relace
Distributed Systems Engineer
Relace
Managed AI model fine-tuning and deployment
About Relace
Relace is building a globally distributed inference cloud. Customers send us requests, we run them on our own GPU fleet around the world, and we compete on price, speed and reliability against companies far larger than us. We have customers and revenue today, we’re venture backed, and the team is still small, so you’ll own whole systems, not slices of them.
We build our own stack, and the fleet runs on Kubernetes across multiple clouds. Performance is the business: ten percent faster is ten percent more revenue. If you like making things fast, this is about as direct as it gets. You can trace a change you shipped this week to what it earned.
Some numbers: we process 800B+ tokens a day, we’ve grown 3x in the last three months, we’re one of the top providers by volume on OpenRouter, and we raised a $23M Series A led by Andreessen Horowitz, with participation from Matrix Partners and Y Combinator.
The role
You would work on how the globally distributed inference runs: the routing layer deciding which machine handles a request, what happens when more work arrives than we have capacity for, and how the fleet grows and shrinks across clouds as demand moves. A lot of it is our own Rust service and the Kubernetes services that manage a multi-cloud fleet.
What we need to see
Around five or more years building real production systems where latency and failure behaviour mattered: serving systems, control planes, schedulers, databases, networking, trading systems. Not data pipelines or analytics.
A systems language you know well. Rust, C++ or Go is good.
Real ownership of something that ran under load, and the ability to talk through a production failure you debugged yourself, in detail.
Kubernetes at the level of someone who has extended it, particularly on low-reliability infrastructure.
Nice to have
Experience with a scheduler or load balancer as a real project rather than a side task; work with spot or preemptible capacity; any exposure to AI serving systems, which is genuinely optional. We can teach you our domain, but we can’t teach distributed systems instincts.
Working here
San Francisco, in person, five days a week, because the hard problems get solved at a whiteboard. This is a senior role, with competitive compensation plus equity.
Relace.ai helps enterprises build and deploy AI models by capturing datasets at the application level, fine-tuning models, and running them with an optimized inference engine. It guides data collection and annotation, model fine-tuning, and deployment to GPU-powered infrastructure, with options for dedicated GPUs or autoscaling deployments and the choice between out-of-the-box hyperparameters or task-specific engineering support. It differentiates itself by offering end-to-end training and deployment plus hands-on ML engineering and flexible infrastructure, with a focus on task-specific optimization and data privacy. Its goal is to enable enterprises to add capable AI systems quickly and reliably through a streamlined process for dataset capture, model tuning, and scalable deployment while keeping data secure.
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