Research Engineer / Performance Engineer, RL Distributed Systems

The shape of it

In the posting’s own words

As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience.

What it asks for · 6

• Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go
• Experience designing, building, and operating large-scale distributed systems in production
• Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery
• Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network
• Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally
• Strong written communication, including design documents and incident writeups

Also a plus

• Experience running ML training or inference infrastructure at scale
• Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration
• Experience building schedulers, autoscalers, or resource management systems
• Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale
• Experience with high-performance networking, RDMA, or collective communication libraries
• Experience building observability or automated remediation for large fleets
• Experience with async Python frameworks such as Trio or asyncio
• Familiarity with reinforcement learning or large language model training workloads

What the job covers

• Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution
• Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination
• Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention
• Design resource management and autoscaling so that compute follows demand as a run's needs shift
• Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results
• Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely
• Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism
• Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build

Tools and skills named

Words the posting leans on

• system16×
• experience11×
• run11×
• failure9×
• design8×
• build7×
• training7×
• large6×
• distributed systems5×
• scale5×
• compute4×
• data4×
• engineers4×
• execution4×
• networking4×
• recovery4×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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