Research Engineer, ML Infrastructure

📋 Description

• • We are an applied AI lab building end-to-end software agents.
• • Our team is extremely talent-dense, with world-class competitive programmers, former founders, and leaders from companies at the cutting edge of AI.
• • 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.
• • WHAT YOU'LL ACCOMPLISH
• • Distributed Training Infrastructure: Build and own the systems that run large-scale training jobs reliably across GPU clusters.
• • Scaling Agent Rollouts: Own the infrastructure that runs hundreds of thousands of concurrent coding agent rollouts in VM sandboxes.
• • Performance Optimization: Profile and improve training throughput end to end.
• • Experiment Orchestration and Tooling: Design and maintain the systems researchers use to launch, track, and analyze experiments.
• • Data Pipeline Engineering: Build high-throughput, reliable data pipelines for training and evaluation.
• • Debugging and Reliability: Diagnose and resolve training failures across GPUs, networking, numerics, and data.
• • Parallelism and Systems Research: Implement and optimize parallelism strategies: data, tensor, pipeline, and sequence parallelism.
• • Scaling Infrastructure Ahead of Research: Anticipate what the research team will need next and build it before it becomes a constraint.
• • EXCEPTIONAL CANDIDATES HAVE DEMONSTRATED
• • Deep experience building and operating distributed training systems for large models.
• • Strong systems engineering fundamentals: distributed systems, networking, storage, and the ability to reason about performance across the full hardware-software stack.
• • Proficiency in Python and C++; experience with PyTorch or equivalent deep learning frameworks at a systems level, not just API usage.
• • Hands-on experience with GPU performance profiling, memory optimization, and compute efficiency.
• • Experience implementing or optimizing parallelism strategies (data, tensor, pipeline, sequence) for large model training.
• • Track record of building tooling and abstractions that meaningfully accelerate research workflows.
• • Strong debugging instincts across complex, distributed systems where failures are non-deterministic and hard to reproduce.
• • Enough ML knowledge to engage substantively with researchers: understand what they are training, why the architecture choices matter, and what the infrastructure needs to support.
• • RESOURCES & ENVIRONMENT
• • Small, highly selective team where research and product move together; prototypes reach real deployment quickly.
• • You'll own and operate infrastructure running across thousands of GPUs; compute is not a constraint and neither is access to the systems you need to do the work well.
• • The environment rewards speed, autonomy, and technical depth with minimal process overhead; this is one of the most competitive and fast-moving problems in AI.
• • EQUAL OPPORTUNITY
• • 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.