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Cognition
San Francisco, California, United States
Source: Cognition careers · View original posting
From Cognition's posting. “We” and “our” refer to the employer.
We are an applied AI lab building end-to-end software agents.
We're the makers of Devin, the first AI software engineer.
Our team is extremely talent-dense. Among our founding team, we have world-class competitive programmers, former founders, and leaders from companies at the cutting edge of AI including Scale AI, Palantir, Cursor, Waymo, Tesla, Lunchclub, Modal, Google DeepMind, and Nuro.
Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason on real-world tasks, apply to join us.
Role Mission
Research moves at the speed of the infrastructure underneath it. Every training run, evaluation loop, and experimental iteration depends on systems that are fast, reliable, and built to scale. This role exists to make sure nothing in the stack becomes the bottleneck that slows down the frontier.
You will own the core systems that researchers depend on daily: distributed training infrastructure, experiment orchestration, data pipelines, and the tooling that turns raw compute into usable research velocity. This is not a support role. You will work directly alongside researchers, understand the science deeply enough to anticipate what they need next, and build systems that hold up under the pressure of training jobs running across thousands of GPUs.
We don't distinguish between research and engineering; the best infrastructure engineers here are also the ones who understand why the research works.
Build and own the systems that run large-scale training jobs reliably across GPU clusters. This includes job launchers, checkpointing and recovery, fault tolerance, and the monitoring that keeps researchers informed and unblocked.
Own the infrastructure that runs hundreds of thousands of concurrent coding agent rollouts in VM sandboxes, from high-fidelity environment design to the distributed systems that hold up at our largest RL training scales.
Profile and improve training throughput end to end. Identify bottlenecks across data loading, communication overhead, memory utilization, and compute efficiency. Implement solutions that meaningfully improve step time and MFU at scale.
Design and maintain the systems researchers use to launch, track, and analyze experiments. Reduce friction in the research loop so that more time is spent on ideas and less on waiting.
Build high-throughput, reliable data pipelines for training and evaluation. Ensure data quality, reproducibility, and efficiency at the scale our training runs demand.
Diagnose and resolve training failures across GPUs, networking, numerics, and data. Maintain detailed understanding of failure modes and build systems that fail gracefully and recover fast.
Implement and optimize parallelism strategies: data, tensor, pipeline, and sequence parallelism. Understand the tradeoffs deeply and apply them to get the most out of available hardware.
Anticipate what the research team will need next and build it before it becomes a constraint. The best infrastructure engineers here are proactive, not reactive.
We care more about demonstrated capability than credentials. A PhD is one signal among many.
Cognition is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law. We are committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process - please let us know if you need any.
LAYIQ is an independent job-discovery service. This listing does not imply a partnership with or endorsement by the employer. Review the original posting for current details and availability.
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